February 6, 2020

Cell Dynamics and Excitable Systems

Wolfgang Losert

University of Maryland | Department of Physics and IREAP

Abstract: The guided migration of cells is a complex dynamical process involving carefully regulated polymerization and depolymerization of the elements of the cellular scaffolding, in particular actin. Recent work has shown that polymerizing and depolymerizing actin can be described as an excitable system which exhibits natural waves or oscillations on scales of hundreds of nm, and that wave-like dynamics can be seen in a wide range of natural contexts. I will show that physical signals nucleate and guide these the wave-like dynamics, and that such guided actin waves control cell migration for a broad range of cell types. This opens up novel approaches to control cell behavior.

February 13, 2020

Modeling the Effects of Thermoregulation on Human Sleep

Shelby Wilson

University of Maryland | Department of Biology

Abstract: Sleep is a behavioral state in which we spend nearly one third of our lives. This biological phenomenon clearly serves an important role in the lives of most species. Here, we present a mathematical model of human sleep- wake regulation with thermoregulatory functions to gain quantitative insight into the effects of ambient temperature on sleep quality. Numerical simulations provide quantitative answers regarding how humans sleep dynamics might adjust in response to being challenged with ambient temperatures away from thermoneutral. We will discuss the dynamics associated with the model as well as how the model could be used as a foundation for in silico simulations pertaining to jet lag, sleep deprivation, and temperature effects on sleep.

February 20, 2020

From atoms to dynamics (with help from statistical physics and AI)

Pratyush Tiwary

University of Maryland | Department of Chemistry & Biochemistry

Abstract: The ability to rapidly learn from high-dimensional data to make reliable predictions about the future of a given system is crucial in many contexts. This could be a fly avoiding predators, or the retina processing terabytes of data almost instantaneously to guide complex human actions. In this work we draw parallels between such tasks, and the efficient sampling of complex molecules with hundreds of thousands of atoms. Such sampling is critical for predictive computer simulations in condensed matter physics and biophysics, including but not limited to problems such as crystal nucleation and drug unbinding. For this we use the Predictive Information Bottleneck (PIB) framework developed and used for the first two classes of problems, and re-formulate it for the sampling of biomolecular structure and dynamics, especially when plagued with rare events, and with minimum assumptions on the physics of the system [1-2]. Our method considers a given biomolecular trajectory expressed in terms of order parameters or basis functions, and uses a deep neural network to learn the minimally complex yet most predictive aspects of this trajectory, viz the PIB. This information is used to perform iterative rounds of biased simulations that enhance the sampling along the PIB to gradually improve its accuracy, directly obtaining associated thermodynamic and kinetic information. We demonstrate the method on different test-pieces, where we calculate the dissociation pathway and timescales slower than milliseconds. These include ligand dissociation from the protein lysozyme and and from flexible RNA.

1. Tiwary and Berne, PNAS 2016

2. Wang, Ribeiro and Tiwary, Nature Commun. 2019

February 27, 2020

Utilizing Noise to Alter Dynamic Response of Coupled Oscillator Arrays

Gizem Acar

University of Maryland | Department of Mechanical Engineering

Abstract: Noise has usually been considered as an unwanted disturbance and considerable research has been done on noise reduction in dynamical systems over the past decades. Alternatively, one can view noise as a means to alter the dynamic response of a nonlinear system. The nonlinear systems of interest are coupled oscillator arrays, which can be used to describe rotary systems and energy harnessing systems. In these systems, response localizations can occur. With an appropriate choice of initial conditions and harmonic input, a coupled oscillator system can be excited to realize a periodic response, wherein one or more oscillators oscillate with much higher amplitudes compared to the rest of the oscillators. This type of spatial localization of energy can be suppressed by introducing Gaussian noise into the system. In this talk, we will focus on guiding the system response between different periodic orbits realized for harmonic forcing, through the addition of Gaussian noise in the input. We also explore how long it takes for the noise to suppress energy localization.

March 5, 2020

The evolving science of your metabolism

James Yorke

University of Maryland | Department of Mathematics and Department of Physics

Abstract: Each year recently I have given a talk about some important topic that is not one of my research areas. This lecture concerns ideas in the science of nutrition and metabolism that I feel most people should know about. We seem to know more about planets circling other stars than about metabolism. And there are good reasons for that.

March 12, 2020

Testing of a Hybrid Modeling Approach for the Prediction of the Atmospheric State by Blending Numerical Modeling and Machine Learning

Istvan Szunyogh

Texas A&M | Department of Atmospheric Sciences

Abstract: The talk is an overview of the latest results of research efforts to apply a hybrid (numerical-machine-learning) modeling approach developed at the University of Maryland to global weather prediction. The forecast performance of the hybrid model is assessed by comparing it to that of persistence, a numerical physics-based model, and a machine learning (ML) model, whose prognostic state variables and resolution are identical to those of the hybrid model. The hybrid model typically provides realistic prediction of the weather for the entire globe for about two-three days. Both the hybrid and the ML model outperform persistence in the extratropics, but not in the tropics. While the relative performance of the ML model compared to the physics-based model is mixed, the hybrid model forecasts are more accurate than either the ML or the physics-based model forecasts at the shorter forecast times. Potential techniques to further improve the short-term hybrid and ML forecasts and extend the valid time of the forecasts are also discussed.

March 19, 2020

Spring Break - No seminar

[CANCELLED] March 26, 2020

Title TBA

Luiz Pessoa

University of Maryland | Department of Psychology and Maryland Neuroimaging Center

Abstract: TBA

[CANCELLED] April 2, 2020

Reservoir Computing for Time Series Prediction: A Tutorial with Tricks of the Trade

Keshav Srinivasan

University of Maryland | Department of Physics and Biophysics Program

Alex Wikner

University of Maryland | Department of Physics and IREAP

Abstract: TBA

[CANCELLED] April 9, 2020

Reconcilable Differences

Yannis Kevrekidis

Johns Hopkins University | Department of Chemical and Biomolecular Engineering

Abstract: I will discuss several old and new examples of extracting dynamic models from data using techniques from manifold learning / machine learning. I will then focus on the problem of matching different models of the same data/phenomenon: the construction of data-driven diffeomorphisms that map different realizations of the same "truth" to each other. I will discuss several different cases: matching models across scales, across fidelities, matching physical models with ML ones, matching different neural network models .... I will also describe a useful tool for the data-driven construction of such "mirrors", matching systems to each other: a local conformational autoencoder.

[CANCELLED] April 16, 2020

Title TBA

Louis Pecora

Naval Research Laboratory

Abstract: TBA

[CANCELLED] April 23, 2020

Title TBA

Joseph Hart

Naval Research Laboratory

Abstract: TBA

[CANCELLED] April 30, 2020

Title TBA

Tamer Zaki

Johns Hopkins University | Department of Mechanical Engineering

Abstract: TBA

[CANCELLED] May 7, 2020

Orbits, caustics, splashback: understanding the dynamics of dark matter particles

Benedikt Diemer

University of Maryland | Department of Astronomy

Abstract: TBA

[via ZOOM] May 14, 2020

Solving hard computational problems with coupled lasers

Nir Davidson

Weizmann Institute of Science

Abstract: Hard computational problems may be solved by realizing physics systems that can simulate them. Here we present a new system of coupled lasers in a modified degenerate cavity that is used to solve difficult computational tasks. The degenerate cavity possesses a huge number of degrees of freedom (300,000 modes in our system), that can be coupled and controlled with direct access to both the x-space and k-space components of the lasing mode. Placing constraints on these components can be mapped to different computational minimization problems. Due to mode competition, the lasers select the mode with minimal loss to find the solution. We demonstrate this ability for simulating XY spin systems and finding their ground state, for phase retrieval, for imaging through scattering medium and more.


January 31, 2019

No seminar

February 7, 2019

Propagation of Ultrashort, Intense Laser Pulses Through the Atmosphere

Joseph Peñano

Naval Research Laboratory

Abstract: The propagation of ultra-short (~100 fs), intense (~10–100 TW/cm2) laser pulses in the atmosphere is rich in nonlinear physics and may have a broad range of applications. Experiments using terawatt pulses with durations less than a picosecond demonstrate the formation and long-distance propagation of plasma and optical filaments, white light generation, and the emission of secondary radiation far from the laser frequency. Controlling the propagation of these laser pulses over long atmospheric paths is scientifically and technologically challenging. In this talk, we discuss the various physical mechanisms governing the atmospheric propagation of ultrashort laser pulses and report on several theoretical, computational, and experimental studies carried out by the Naval Research Laboratory (NRL). These studies include recent experiments demonstrating extended channeling through very strong atmospheric turbulence enabled by nonlinear self-focusing of laser pulses in air. In addition, we discuss theoretical considerations for increasing the laser power that can propagated through the atmosphere.

February 14, 2019

Time-delayed optoelectronic oscillators: theory and applications

Yanne Chembo

University of Maryland | Department of Electrical and Computer Engineering/IREAP

Abstract: Time-delayed optoelectronic oscillators (OEOs) are at the center of a very large body of scientific literature. The complex behavior of these nonlinear oscillators has been thoroughly explored both theoretically and experimentally, leading to a better understanding of their dynamical properties. Beyond fundamental research, these systems have also inspired a wide and diverse set of applications, such as optical chaos communications, pseudo-random number generation, optoelectronic reservoir computing, ultra-pure microwave synthesis, optical pulse-train generation, and sensing. In this communication, we will provide a comprehensive overview of this field, outline the latest achievements, and discuss the main challenges ahead.

February 21, 2019

Quantum Lyapunov Spectrum

Brian Swingle

University of Maryland | Department of Physics

Abstract: Positive Lyapunov exponents are one of the key characteristics of chaos in classical dynamical systems. Here we discuss the notion of Lyapunov exponents in quantum many-body systems focusing on a recent definition of a whole spectrum of quantum Lyapunov exponents (https://arxiv.org/abs/1809.01671). The talk will not assume prior knowledge of the subject, although some knowledge of quantum mechanics will be helpful.

February 28, 2019

Lagrangian chaos and passive scalar turbulence

Jacob Bedrossian

University of Maryland | Department of Mathematics

Abstract: The purpose of this work is to perform a mathematically rigorous study of Lagrangian chaos and passive scalar turbulence in incompressible fluid mechanics. We study the Lagrangian flow associated to velocity fields arising from various models of fluid mechanics subject to white-in-time, Sobolev-in-space stochastic forcing in a periodic box. We prove that if the forcing satisfies suitable non-degeneracy conditions, then these flows are chaotic in the sense that the top Lyapunov exponent is strictly positive. Our main results are for the 2D Navier-Stokes equations and the hyper-viscous regularized 3D Navier-Stokes equations (at arbitrary Reynolds number and hyper-viscosity parameters). For the passive scalar problem, we study statistically stationary solutions to the advection-diffusion equation driven by these velocities and subjected to random sources. The chaotic Lagrangian dynamics are used to prove a version of anomalous dissipation in the limit of vanishing diffusivity, which in turn, implies that the scalar satisfies Yaglom's 1949 law of passive scalar turbulence in over a suitable inertial range -- the constant flux law analogous to the Kolmogorov 4/5 law. To our knowledge, this work is the first to provide a complete mathematical proof of any such scaling law from fundamental equations of fluid mechanics. The work combines ideas from random dynamical systems (the Multiplicative Ergodic Theorem and an infinite dimensional variation of Furstenberg's Criterion) with elementary approximate control arguments and infinite-dimensional hypoellipticity via Malliavin calculus. Joint work with Alex Blumenthal and Sam Punshon-Smith.

March 7, 2019

APS March Meeting - No seminar

March 14, 2019

Reaction fronts and swimming organisms in laminar flows: manifolds and barriers

Tom Solomon

Bucknell University | Department of Physics

Abstract: We present experiments on the effects of laminar flows on the spreading of the excitable Belousov-Zhabotinsky chemical reaction and on the motion of swimming bacteria. The results of these experiments have applications for a wide range of systems including microfluidic chemical reactors, cellular-scale processes in biological systems, and blooms of phytoplankton in the oceans. To predict the behavior of reaction fronts, we adapt tools used to describe chaotic fluid mixing in laminar flows. In particular, we propose "burning invariant manifolds" (BIMs) that act as one-way barriers that locally block the motion of reaction fronts. These barriers are measured experimentally in a range of vortex-dominated 2- and 3-dimensional fluid flows. A similar theoretical approach predicts "swimming invariant manifolds" (SwIMs) that are one-way barriers the impede the motion of microbes in a flow. We are conducting experiments to test the existence of SwIMs for both wild-type and smooth swimming Bacillus subtilis in hyperbolic and vortex-dominated fluid flows.

March 21, 2019

Spring Break - No seminar

March 28, 2019

Turbulence and dynamo effect in electronic materials

Victor Galitski

University of Maryland | Department of Joint Quantum Institute

Abstract: The dynamo effect is a class of macroscopic phenomena responsible for generation and maintaining magnetic fields in astrophysical bodies. It hinges on hydrodynamic three-dimensional motion of conducting gases and plasmas that achieve high hydrodynamic and/or magnetic Reynolds numbers due to large length scales involved. The existing laboratory experiments modeling dynamos are challenging and involve large apparatuses containing conducting fluids subject to fast helical flows. Here we propose that electronic solid-state materials -- in particular, hydrodynamic metals -- may serve as an alternative platform to observe some aspects of the dynamo effect. In this talk, I will discuss two candidate systems -- Well semimetals and critical fluctuating superconductors, where electronic turbulence and dynamo effect appear within experimental reach.

[1] V. Galitski, M. Kargarian, and S. Syzranov, "Dynamo Effect and Turbulence in Hydrodynamic Weyl Metals," Phys. Rev. Lett. 121, 176603 (2018)

[2] Y. Liao and V. Galitski, "Two-Fluid Hydrodynamics and Viscosity Suppression in Fluctuating Superconductors,"


April 4, 2019

'We have no good fundamental theory (of turbulence) at all': Was Feynman right?

Gregory Eyink

Johns Hopkins University | Department of Applied Mathematics

Abstract: In his famous undergraduate physics lectures, Richard Feynman remarked about the problem of fluid turbulence: "Nobody in physics has really been able to analyze it mathematically satisfactorily in spite of its importance to the sister sciences.” This statement was already false when Feynman made it. Unbeknownst to him, Lars Onsager decades earlier had made an exact mathematical analysis of the high Reynolds-number limit of incompressible fluid turbulence, using a method that would now be described as a non-perturbative renormalization group analysis and discovering the first “conservation-law anomaly” in theoretical physics. Onsager’s results were only cryptically announced in 1949 and he never published any of his detailed calculations. Onsager’s analysis was finally rescued from oblivion and reproduced by the speaker in 1994. The ideas have subsequently been intensively developed in the mathematical PDE community, where deep connections emerged with John Nash’s work on isometric embeddings. Furthermore, the method has more recently been successfully applied to new physics problems, compressible fluid turbulence and relativistic fluid turbulence, yielding many new testable predictions. This talk will briefly review Onsager’s exact analysis of the original incompressible turbulence problem and subsequent developments. Then a new application to kinetic plasma turbulence will be described, with novel predictions for turbulence in nearly colllisionless plasmas such as the solar wind and the terrestrial magnetosheath.

April 11, 2019

Chaotic Dynamics of Driven Graphene Josephson Junctions

James Williams

University of Maryland | Department of Physics

Abstract: Josephson junctions with topological materials as weak links pervade the research of Majorana bound states. Yet these junctions exhibit many complex phenomena, some which are accessible due to new material and fabrication technology. A comprehensive picture is important both for elucidation of the physics of Majorana bound states and fundamental research into Josephson junctions. In this talk I will detail the physics of Josephson junctions and the chaotic behavior we observe in high-quality, graphene-based Josephson junctions under application of RF radiation. We quantify a instability measured in the AC Josephson regime which is analyzed in terms of crisis-induced intermittency. Further, these observations cast doubt over arguments that AC Josephson effect in the low RF drive amplitude region would offer the opportunity to observe 4-π current phase relation in topological Josephson junctions.

April 18, 2019

The physical processes of brain waste removal

Douglas Kelley

University of Rochester | Department of Mechanical Engineering

Abstract: The human brain accounts for just 2% of the body's mass but metabolizes 25% of its calories, producing significant metabolic waste. However, waste buildup links to neurodegenerative diseases like Alzheimer's and Parkinson's. The brain removes waste via the recently-discovered glymphatic system, a combination of spaces and channels through which cerebrospinal fluid flows to sweep away toxins like amyloid-beta. With an interdisciplinary group of neuroscientists and physical scientists, I study the physical processes of the glymphatic system: Where does fluid flow, and how fast? What drives flow? Does flow shear cause waste accumulation? What characteristics of the system enable essential functions? How can we improve waste removal? Can we use glymphatic flow to deliver drugs? The team combines physics tools like particle tracking and newly-invented front tracking with biological tools like two-photon imaging through cranial windows in order to address these questions with in vivo flow measurements. I will talk about recent results showing that glymphatic flow proceeds along vessels with near-optimal shapes, pulses with the heart, is driven by artery walls, and can be manipulated by changing the wall motion.

April 25, 2019

Controlling dynamical systems using a deep reservoir computer

Daniel Gauthier

Ohio State University | Department of Physics

Abstract: I will describe the design of a smart controller for dynamical systems based on reservoir computers. I use this approach to control fixed points, unstable period orbits, and arbitrary orbits for the Lorenz chaotic system. We have also applied the controller to other systems, such as a mathematical model of a drone (quadcopter). The controller to easily adopt to a partial degradation of the system allowing the drone to maintain stable flight. I will discuss our progress on using this approach to controlling the dynamics of a chaotic electronic circuit.

May 2, 2019

Cluster Synchronization in Multilayered Networks

Louis Pecora

Naval Research Laboratory

Abstract: The behavior of dynamical systems (nodes or oscillators) that are coupled together in complex networks are greatly affected by the structure of those networks. Various patterns of synchronization among subsets of nodes are possible when the network has symmetries and input or balanced partitions. Symmetries and partitions will be introduced in this talk along with the dynamical patterns they can support. The basic machinery from group theory will be used to analyze the patterns, especially for their dynamical stability. The main portion of the talk will be about extending these ideas to networks with multiple oscillator types called multilayered networks. This extension while looking straightforward has some interesting structures that govern the behavior of synchronous clusters in the network as a whole as well as the symmetries of the network.

May 9, 2019

Identifying and Harnessing Attractor Dynamics in Neural Systems with Applications to Chaotic Time-Series Prediction and Turing-Complete Program Learning

Garrett Katz

Syracuse University | Department of Computer Science

Abstract: In this talk, we will describe two branches of research related to neural attractor dynamics: the first focuses on *identifying* attractors, in order to understand what a trained network has learned; and the second focuses on *harnessing* attractors, in order to teach a network useful computations. More specifically, the first part of this talk presents "directional fibers," which are mathematical objects that can be used to systematically enumerate fixed points in many dynamical systems. Directional fibers are curves in high-dimensional state space that contain the fixed points of a system and can be numerically traversed. We will define directional fibers, derive their important theoretical properties, and describe empirical results of applying directional fibers to locate fixed points in recurrent neural networks. For example, directional fibers revealed that a network trained on the Lorenz system will have fixed points in correspondence with the Lorenz system fixed points, even though the Lorenz fixed points were not included in the training data. The second part of this talk presents a "Neural Virtual Machine" (NVM), which is a purely neural system that can emulate a Turing-complete computer architecture. The NVM uses local learning rules and itinerant neural attractor dynamics to represent, learn, and execute symbolic computer programs written in an assembly-like language. We will present the dynamical equations of the NVM, explain how it can be used to carry out algorithmic tasks, and present results of computer experiments that quantify its performance and scaling requirements. In particular, we demonstrate that the number of neurons required is only linear in the size of the programs being emulated.

May 16, 2019

New methods for fractal and Wada basins

Álvar Daza

King Juan Carlos University | Applied Physics

Abstract: In dynamical systems, basins of attraction are defined as the set of initial conditions leading to a particular asymptotic behavior. Nonlinear systems often give rise to fractal boundaries in phase space, hindering predictability. A special case of fractal boundaries appears when a single boundary separates three or more different basins of attraction. Then we say that the set of basins has the Wada property and initial conditions near that boundary become particularly unpredictable. Although it could seem an odd situation, many physical systems showing this topological property appear in the literature. In this talk, I will review some basic aspects on Wada basins, and then I will describe some new recently developed methods to ascertain the Wada property in dynamical systems. These new methods present important advantages with respect to the previously known method, provide new perspectives on the Wada property and broaden the situations where it can be verified. Also, I will show how the novel concept of basin entropy helps us quantify the unpredictability associated to different basins of attraction and its relation with Wada basins.

September 5, 2019

Population collapse in elite-dominated societies: A differential equation model without differential equations

James Yorke and Naghmeh Akhavan

University of Maryland | Department of Mathematics

Abstract: We discuss models of interactions with the environment by human populations, both between poor and rich people, "Commoners'' and "Elites''. The Elites control the society's wealth and consume it at a higher rate than Commoners, whose work produces the wealth. We say a model is “Elite-dominated” when the Elites' per capita population change rate is always at least as large as the Commoners'. We can show the model always exhibits population crashes for all choices of parameter values for which it is Elite-dominated. But any such model with explicit equations raises questions of how the resulting behaviors depend on the details of the models. How important are the particular design features codified in the differential equations? We discard the differential equations, replacing them with qualitative conditions that the original model satisfies, and we prove these conditions imply population collapse must occur. In particular, one condition is that the model is Elite-dominated. Our approach of introducing qualitative mathematical hypotheses can better show the underlying features of the model that lead to collapse. We also ask how societies can avoid collapse.

September 12, 2019

Strong neuron-to-body coupling implies weak neuron-to-neuron coupling in motor cortex

Woodrow Shew

University of Arkansas | Department of Physics

Abstract: Cortical neurons can be strongly or weakly coupled to the network in which they are embedded, firing in sync with the majority or firing independently. Both these scenarios have potential computational advantages in motor cortex. Commands to the body might be more robustly conveyed by a strongly coupled population, whereas a motor code with greater information capacity could be implemented by neurons that fire more independently. Which of these scenarios prevails? Here we measure neuron-to-body coupling and neuron-to-population coupling for neurons in motor cortex of freely moving rats. We find that neurons with high and low population coupling coexist, and that population coupling was tunable by manipulating inhibitory signaling. Importantly, neurons with different population coupling tend to serve different functional roles. Those with strong population coupling are not involved with body movement. In contrast, neurons with high neuron-to-body coupling are weakly coupled to other neurons in the cortical population.

September 19, 2019

No seminar. We invite you to attend the Paint Branch Distinguished Lecture at 4pm, 1101 A. James Clark Hall.

Paint Branch Distinguished Lecture: From Nonlinear Optics to High-Intensity Laser Physics

Donna Strickland

University of Waterloo | Department of Physics and Astronomy

Abstract: The laser increased the intensity of light that can be generated by orders of magnitude and thus brought about nonlinear optical interactions with matter. Chirped pulse amplification, also known as CPA, changed the intensity level by a few more orders of magnitude and helped usher in a new type of laser-matter interaction that is referred to as high-intensity laser physics. In this talk, I will discuss the differences between nonlinear optics and high-intensity laser physics. The development of CPA and why short, intense laser pulses can cut transparent material will also be included. I will also discuss future applications.

September 26, 2019

Tree-like approximations and critical network cascades

Sarthak Chandra

University of Maryland | Department of Physics

Abstract: Network science is a rapidly expanding field, with a large and growing body of work on network-based dynamical processes. Most theoretical results in this area rely on the so-called "locally tree-like approximation" (which assumes that one can ignore small loops in a network). This is, however, usually an `uncontrolled' approximation, in the sense that the magnitudes of the error are typically unknown, although numerical results show that this error is often surprisingly small. In our work, we place this approximation on more rigorous footing by calculating the magnitude of deviations away from tree-based theories in the context of network cascades (i.e., a network dynamical process describing the spread of activity through a network). For this widely applicable problem, we discuss the conditions under which tree-like approximations give good results, and also explain the reasons for deviation from this approximation. More specifically, we show that these deviations are negligible for networks with a large number of network links, justifying why tree-based theories appear to work well for most real-world networks.

October 3, 2019

Can we use future observations to improve current forecasts without cheating?

Slides available here.

Eugenia Kalnay

University of Maryland | Department of Civil and Environmental Engineering, Department of Mechanical Engineering, Department of Atmospheric and Oceanic Science

Abstract: Co-authored with Tse-Chun Chen and Daisuke Hotta. The National Weather Service computes operational weather forecasts using a process called “data assimilation”: A 6 hour forecast is computed starting from the current “analysis”. The 6 hour forecast is then optimally combined with the observations collected 6 hours later to create the new analysis which serves as initial conditions for the next forecast. This process, known as “analysis cycle”, is repeated every 6 hours. Miyakoda (personal communication, ~1980) pointed out that using any future information to improve current forecasts should be considered “cheating” because it cannot be done in operational forecasting. Chen (2018, PhD thesis), Chen and Kalnay (2019a) MWR, and Chen and Kalnay (2019b, under review), developed an application of Ensemble Forecast Sensitivity to Observations (EFSO, Kalnay et al., 2012, Tellus) combined with Proactive Quality Control (PQC, Hotta et al., 2017). It uses future data (e.g., observations obtained 6 hours after the present analysis) to identify and delete current detrimental observations (in the present analysis). We found that making a late correction of every current analysis after the new observations have been received, accumulates improvements with time. The accumulated improvement is found to be much larger than the last correction that cannot be used in order to avoid cheating, so that forecasts are significantly improved “without cheating”.

October 10, 2019

Quantum impulse control

Christopher Jarzynski

University of Maryland | Department of Chemistry & Biochemistry and Department of Physics

Abstract: The quantum adiabatic theorem governs the evolution of a wavefunction under a slowly time-varying Hamiltonian. I will consider the opposite limit of a Hamiltonian that is varied impulsively: a strong perturbation U(x,t) is applied over a time interval of infinitesimal duration ε → 0. When the strength of the perturbation scales like 1/ ε 2, there emerges an interesting dynamical behavior characterized by an abrupt displacement of the wave function in coordinate space. I will solve for the evolution of the wavefunction in this situation. Remarkably, the solution involves a purely classical construction, yet describes the quantum evolution exactly, rather than approximately. I will use these results to show how appropriately tailored impulses can be used to control the behavior of a quantum wavefunction.

October 17, 2019

Using machine learning to assess short term causal dependence and infer network links

Amitava Banerjee

University of Maryland | Department of Physics and IREAP

Abstract: The general problem of determining causal dependences in an unknown time evolving system from observations is of great interest in many fields. Examples include inferring neuronal connections from spiking data, deducing causal dependences between genes from expression data, discovering long spatial range influences in climate variations, etc. Previous work has tackled such problems by consideration of correlations, prediction impact, or information transfer metrics. Here we propose a new method that leverages the ability of machine learning to generalize from examples, combined with concepts from dynamical systems theory. We test our proposed technique on numerical examples obtaining results that suggest excellent performance for a large range of situations. An important, somewhat surprising, conclusion is that, although our rationale is based on noiseless deterministic systems, dynamical noise can greatly enhance our technique's effectiveness.

October 24, 2019

Observations of Laminar Chaos

Rajarshi Roy

University of Maryland | Department of Physics and IREAP and IPST

Abstract: TBD

October 31, 2019

A putative mechanism for implicit learning in biological and artificial neural systems

Zhixin Lu

University of Pennsylvania | School of Engineering and Applied Science

Abstract: The human brain is capable of diverse feats of intelligence. A particularly salient example is the ability to implicitly learn dynamics from experiencing the physical world. Analogously, artificial neural systems such as reservoir computing (RC) networks have shown great success in learning the long-term behavior of various complex dynamical systems from data, without knowing the explicit governing equation. Regardless of the marked differences between biological and artificial neural systems, one fundamental similarity is that they are essentially dynamical systems that are fine-tuned towards the imitation of other dynamical systems. To shed some light on how such a learning function may emerge from biological systems, we draw inspiration from observations of the human brain to propose a first-principles framework explicating its putative mechanisms. Within this framework, one biological or artificial dynamical system, regardless of its specific composition, implicitly and adaptively learns other dynamical attractors (chaotic or non-chaotic) by embedding the dynamical attractors into its own phase space through the invertible generalized synchronization, and imitates those attractors by sustaining the embedded attractors through fine-tuned feedback loops. To demonstrate this general framework, we construct several distinct neural network models that adaptively learn and intimate multiple attractors. With these, we observe and explain the emergence of five distinct phenomena reminiscent of cognitive functions: (i) imitation of a dynamical system purely from learning the time series, (ii) learning of multiple dynamics by a single system, (iii) switching among the imitations of multiple dynamical systems, either spontaneously or driven by external cues, (iv) filling-in missing variable from incomplete observations of a learned dynamical system, and (v) deciphering superimposed input from different dynamical systems.

November 7, 2019

Nonlinear and quantum phenomena in whispering-gallery mode crystalline resonators

Yanne Chembo

University of Maryland | Department of Electrical and Computer Engineering and IREAP

Abstract: Whispering-gallery mode (WGM) resonators are disks, toroids or spheres with micro- or millimetric radius and (sub-)nanometer surface roughness. They have the capability to trap laser light by total internal reflection for a duration higher than a microsecond. In these ultra-high Q resonators, the small volume of confinement, high photon density and long photon lifetime ensures a very strong light-matter interaction, which may excite the WGMs through various nonlinear effects, namely Kerr, Raman, or Brillouin. Quantum phenomena such as twin-photon generation, entanglement, and squeezing can also occur in these optical cavities. In this talk, we discuss some of the main challenges related to the understanding of nonlinear and quantum phenomena in WGM resonators, and present as well as some of the principal applications in aerospace and communication engineering.

November 14, 2019

Data-Assisted Forecasting of Chaotic Dynamical Systems using Partial State Measurements

Jaideep Pathak

University of Maryland | Department of Physics

Abstract: We consider the problem of data-driven forecasting of chaotic dynamical systems when the available data is from a sparse spatial sampling, i.e., the full state of the dynamical system cannot be observed directly. Recently, there have been several promising data-driven approaches to forecasting of chaotic dynamical systems using machine learning. Particularly promising among these are hybrid approaches that combine machine learning with a knowledge-based model, where a machine learning technique is used to correct the imperfections in the knowledge-based model. Such a hybrid approach is promising when a knowledge-based model is available but is imperfect due to incomplete understanding of the physical processes in the underlying dynamical system. However, previously proposed data-driven forecasting approaches assume knowledge of the full state of the dynamical system. We seek to relax this assumption by using a data assimilation technique along with Machine Learning in a novel technique that improves forecasts. We demonstrate that using partial measurements of the state of the dynamical system we can train a machine learning model to correct model error in an imperfect knowledge-based model.

November 21, 2019

Cybersecurity Applications of Chaos

Andrew Pomerace

Potomac Research LLC

Abstract: In this talk, I will describe experiments on a chaotic electronic circuit that can be used as a high speed true random number generator. This circuit can be modified to act as a Physically Unclonable Function, which are novel cybersecurity devices used for device authentication, tamper-proofing, and key generation.

November 28, 2019

Thanksgiving Break - No seminar

December 5, 2019

Scaling and universality in bistable optical cavities

Said Rodriguez

AMOLF | Interacting Photons (Group Leader)

Abstract: Driven nonlinear dynamical systems can reside in two steady states at a single driving condition. This feature, known as bistability, is associated with emergent phenomena in phase transitions, scaling, and universal behavior. In descriptions of bistable systems, it is typically assumed that the nonlinear force responsible for bistability acts instantaneously on the system. In addition, the role of quantum fluctuations on bistability was until recently largely assumed to be irrelevant to experiments. In this talk, I will present two experiments where these two assumptions were challenged. Both of these experiments were based on nonlinear optical cavities driven by light, but similar physics is expected in other systems. The experiments we performed consisted of scanning a driving parameter (e.g. laser intensity or frequency) across an optical bistability at various speeds, and analyzing the resultant dynamic optical hysteresis. Intriguingly, both quantum fluctuations and non-instantaneous interactions lead to a universal power law decay of the hysteresis area as a function of the scanning speed. However, whereas quantum fluctuations lead to universal scaling behavior in the limit of slow scans, non-instantaneous interactions lead to a universal scaling behavior in the limit of fast scans. I will conclude with perspectives for realizing lattices of bistable optical cavities, and the opportunities that these open for performing analog computation and for studying stochastic nonlinear dynamics with light.

February 1, 2018

Reviewing artificial intelligence and the book Life 3.0

Jim Yorke

University of Maryland | Department of Mathematics

Abstract: TBA


February 8, 2018

Experiments with arbitrary networks in time-multiplexed delay systems

Joe Hart

University of Maryland | Department of Physics ; IREAP

Abstract: Complex networks of coupled oscillators have proven to be systems that can display incredibly rich dynamical behaviors. Despite great theoretical advances in our understanding of coupled oscillator networks, it has proven difficult to design experiments that permit the study of dynamics on large networks with arbitrary topology. Here we present a new experimental approach that allows for the investigation of large networks of truly identical nodes with arbitrary topology. Our approach relies upon the space-time interpretation of systems with time delay in order to construct a network of coupled maps using a single nonlinear, time-delayed feedback loop. This system has many advantages: the network nodes are truly identical, the network is easily reconfigurable, and the network dynamics occur at high speeds. We use this system to study cluster synchronization and chimera states in both small and large networks of different topologies.


February 15, 2018

The spherical Couette system: simple yet complex

Ankit Barik

Johns Hopkins University | Department of Earth & Planetary Sciences

Abstract: The spherical Couette system consists of two concentric spheres rotating differentially about a common axis. The space in between the spheres is filled with a conducting fluid. It is a relatively simple system without any thermal or density stratification and and has potential applications to planetary and stellar interiors. In addition, it is also an extremely interesting fluid dynamical system displaying a host of complex instabilities and other fluid dynamics phenomena. In the first part of the talk, I shall introduce the system and explore the generic regimes of different instabilities. This will be followed by some results using hydrodynamic simulations of this system with two pseudo-spectral codes MagIC and XSHELLS while comparing them with experimental data. Focus will be on the origin of special wave instabilities called 'inertial modes' and the transition to turbulence. In the next part, I will present magnetohydrodynamic simulations exploring the effect of an external magnetic field on inertial modes. In the final part of the talk, I will present simulations of self-consistent dynamo action in this system and the parameter dependence of the same.


February 22, 2018

Classical-to-quantum correspondence and transitions in chaotic dynamics of out-of-time-ordered correlators

Victor Galitski

University of Maryland | Department of Physics

Abstract: One of the most intriguing phenomena in the studies of classical chaos is the butterfly effect, which manifests itself in that small changes in initial conditions lead to drastically different trajectories. It is characterized by a Lyapunov exponent that measures divergence of the classical trajectories. The question how/if this prototypical effect of classical chaos theory generalizes to quantum systems (where the notion of a trajectory is undefined) has been of interest for decades, but became more popular recently, when it was realized that there exist intriguing connections to string theory and general relativity in some quantum chaotic models. At the center of this activity is the so-called out-of-time-ordered correlator (OTOC) - a quantity that in the classical limit seems to approximate the classical Lyapunov correlator. In this talk, I will discuss the connection between the standard Wigner-Dyson approach to "quantum chaos" and that based on the OTOC on the example of a chaotic billiard and a disordered interacting electron system (i.e., a metal). I will also consider the standard model of quantum and classical chaos - kicked rotor - and calculate the correlator and Lyapunov exponents. The focus will be on how classical chaos and Lyapunov divergence develop in the OTOC and cross-over to the quantum regime. We will see that the quantum out-of-time-ordered correlator exhibits a clear singularity at the Ehrenfest time, when quantum interference effects sharply kick in: transitioning from a time-independent value to its monotonous decrease with time. In conclusion, I will discuss many-body generalizations of such quantum chaotic models.


March 1, 2018

Economic inequality from a statistical physics point of view

Victor Yakovenko

University of Maryland | Department of Physics

Abstract: Inequality is an important and seemingly inevitable aspect of the human society. Various manifestations of inequality can be derived from the concept of entropy in statistical physics. In a stylized model of monetary economy, with a constrained money supply implicitly reflecting constrained resources, the probability distribution of money among the agents converges to the exponential Boltzmann-Gibbs law due to entropy maximization. Our empirical data analysis [1] shows that income distributions in the USA, European Union, and other countries exhibit a well-defined two-class structure. The majority of the population (about 97%) belongs to the lower class characterized by the exponential ("thermal") distribution. The upper class (about 3% of the population) is characterized by the Pareto power-law ("superthermal") distribution, and its share of the total income expands and contracts dramatically during booms and busts in financial markets. Interestingly, the same equations can be also applied to heavy-ion collisions [2]. Globally, energy consumption (and CO2 emissions) per capita around the world shows decreasing inequality in the last 30 years and convergence toward the exponential probability distribution, as expected from the maximal entropy principle. In agreement with our prediction [3], a saturation of the global Gini coefficient for energy consumption at 0.5 is observed for the most recent years. All papers are available at http://physics.umd.edu/~yakovenk/econophysics/.

[1] Yong Tao et al., "Exponential structure of income inequality: evidence from 67 countries", Journal of Economic Interaction and Coordination (2017) http://doi.org/10.1007/s11403-017-0211-6 http://arxiv.org/abs/1612.01624

[2] Xuejiao Yin et al., "A new two-component model for hadron production in heavy-ion collisions", Advances in High Energy Physics (2017) 6708581, http://doi.org/10.1155/2017/6708581

[3] S. Lawrence, Q. Liu, and V. M. Yakovenko, "Global inequality in energy consumption from 1980 to 2010", Entropy 15, 5565 (2013), http://dx.doi.org/10.3390/e15125565


March 8, 2018

APS March Meeting - No seminar


March 15, 2018

Optimal control of networks: energy scaling and open challenges

Francesco Sorrentino

University of New Mexico | Department of Mechanical Engineering

Abstract: Recent years have witnessed increased interest from the scientific community regarding the control of complex dynamical networks. Some common types of networks examined throughout the literature are power grids, communication networks, gene regulatory networks, neuronal systems, food webs, and social systems. Optimal control studies strategies to control a system that minimize a cost function, for example the energy that is required by the control action.We show that by controlling the states of a subset of the nodes of a network, rather than the state of every node, the required energy to control a portion of the network can be reduced substantially. The energy requirements exponentially decay with the number of target nodes, suggesting that large networks can be controlled by a relatively small number of inputs, as long as the target set is appropriately sized. An important observation is that the minimum energy solution of the control problem for a linear system produces a control trajectory that is nonlocal. However, when the network dynamics is linearized, the linearization is only valid in a local region of the state space and hence the question arises whether optimal control can be used. We provide a solution to this problem by determining the region of state space where the trajectory does remain local and so minimum energy control can still be applied to linearized approximations of nonlinear systems. We apply our results to develop an algorithm that determines a piecewise open-loop control signal for nonlinear systems. Applications include controlling power grid dynamics and the regulatory dynamics of the intracellular circadian clock. This work is in collaboration with Isaac Klickstein and Afroza Shirin (UNM).


March 22, 2018

Spring Break - No seminar


March 29, 2018

Magnetic Reconnection and Particle Energization

Marc Swisdak

University of Maryland | IREAP

Abstract: In many plasmas in nature magnetic reconnection is the primary process whereby energy stored in the magnetic field is transformed into kinetic and thermal energy. It lies at the heart of such phenomena as disruptions in fusion experiments, auroras, and solar flares. In addition, it is strongly suspected that reconnection plays a significant role in generating energetic particles (i.e., non-thermal power laws) in these and other systems. I will discuss the basic physics of magnetic reconnection as well as some of the numerical tools (e.g., particle-in-cell simulations) used to study its complex interplay of scales. Finally, I will discuss recent advances in quantifying how and under what conditions reconnection accelerates particles to non-thermal energies.


April 5, 2018

Large-scale neural network modeling: from neuronal microcircuits to whole-brain complex network dynamics

Qin Liu

University of Maryland | Department of Physics

Abstract: Neural networks mediate human cognitive functions, such as sensory processing, memory, attention, etc. Computational modeling has proved to be a powerful tool to test hypotheses of network mechanisms underlying cognitive functions, and to better understand human neuroimaging data. Here we present a large-scale neural network modeling study of human brain visual/auditory processing and how this process interacts with memory and attention. We show how our modeling and simulation can relate phenomena across different scales from the neuronal level to the whole-brain network level. The model can perform a number of cognitive tasks utilizing different cognitive functions by only changing a task-specification parameter. Based on the performance and simulated imaging results of these tasks, we proposed hypothesis for the neural mechanisms underlying several important cognitive phenomena, which could be tested experimentally in the future.


April 12, 2018

Macroscopic Behavior of Systems of Many Interacting Orientable units: The Strong Influence of Dimensionality on the Dynamics

Sarthak Chandra

University of Maryland | Department of Physics and IREAP

Abstract: The Kuramoto model, originally motivated by the dynamics of many interacting oscillators, has been used and generalized for a wide range of applications involving the collective behavior of large heterogenous groups of dynamical units whose states are characterized by a scalar angle variable. One such application in which we are interested is the alignment of velocity vectors among members of a swarm. Despite being commonly used for this purpose, the Kuramoto model can only describe swarms in 2 dimensions, and hence the results obtained do not apply to the often relevant situation of swarms in 3 dimensions. Partly based on this motivation, we study the Kuramoto model generalized to D dimensions, focusing on the 3-dimensional case. We show that in 3 dimensions, as well as for all odd dimensionality, the generalized Kuramoto model for heterogenous units has dynamics that are remarkably different from the dynamics in 2 dimensions. In particular, for odd D the transition of the time asymptotic equilibrium state to coherence occurs discontinuously as the coupling constant K is increased through zero, as opposed to the D=2 case (and, as we will show, also the case of even D) for which the transition to coherence occurs as K increases through a postitive critical value Kc. We observe that the odd-dimensional Kuramoto models with a large number of swarm elements is not low dimensional in the sense of Ott & Antonsen (2008). However, application of our generalized form of the Ott-Antonsen ansatz does reduce the complexity of the problem when compared with the full system of equations. We generalize our results beyond the Kuramoto model to a wider class of swarm dynamics in high dimensions, and we show that the Ott-Antonsen ansatz can be appropriately generalized for this class of systems. We expect that our results will hence be useful for solving questions involving coupled systems on a sphere in high dimensions, beyond just the Kuramoto model.


April 19, 2018

Turbulence closure ideas from plasma physics

William Dorland

University of Maryland | Department of Physics

Abstract: First-principles turbulence simulations are expensive but very useful in many contexts. One approach to improving one's ability to predict experimental or observational data is to design algorithms for ever more processors, allowing ever higher (and more realistic) resolution. But one can also try to work in the opposite direction, developing closures to reduce the resource demands of computations. In a typical high-resolution turbulence simulation that I undertake, there are O(10**9) spectral amplitudes. Only a tiny percentage of these modes are excited to any appreciable level. I will discuss (and seek advice from the audience!) approaches we are pursuing to develop algorithms that solve a closed (or somewhat reduced) first-principles system with as little resolution as should be required.


April 26, 2018

High-speed prediction of a chaotic system using reservoir computers

Dan Gauthier

Ohio State University | Department of Physics

Abstract: A reservoir computer is an approach to machine learning that appears to be ideally suited for classifying time varying signals or as a black-box system for forecasting the behavior of a dynamical system. It consists of a recurrent artificial neural network that serves as a “universal” dynamical system into which data are input, where the connections on the input layer and recurrent links within the network are chosen randomly and held fixed. Only the weights of network output layer are adjusted during the training period, which greatly reduces the training time. I will discuss our recent progress on realizing high-speed prediction of the Mackey-Glass chaotic system (>10^8 predictions per second) using a reservoir computer based on a time-delay autonomous Boolean network realized on a field programmable gate array. I will also touch on our efforts to control a dynamical system with a reservoir computer and some recent results on methods to identify the optimum size of the reservoir computer network for a given task.


May 3, 2018

Heterogenous chaotic attractors

Jim Yorke

University of Maryland | Department of Mathematics

Abstract: There is a saying: "there are two kinds of people in the world—the simple-minded and the muddle-headed." I prefer the former. But much of the investigation of chaotic attractors uses models that are sometimes overly simplistic at least for studying high dimensional chaotic attractors. Our goal is to produce more models that are still simplistic but better reflect typical high dimensional chaotic attractors and permit a better but still simple-minded understanding of chaos in high dimensions. This is a report on joint work with Miguel Sanjuan and Yoshi Saiki.


May 10, 2018

No seminar


September 13, 2018

No seminar


September 20, 2018

Statistical Description of Hamiltonian Mixed Phase Space Systems

Prof. Shmuel Fishman

Technion University | Department of Physics

Abstract: Typical physical systems follow deterministic behavior. This behavior can be sensitive to initial conditions, such that it is very difficult to predict their behavior in the longtime limit. The resulting motion is chaotic and looks stochastic or random. In many cases the motion is described by a Hamiltonian and the energy is conserved. The motion can be also regular, that is predictable. In the work reported here we studied systems where depending on initial conditions the motion is either regular or chaotic. The simplest systems of this type are of two degrees--of--freedom, or periodically kicked systems with one degree--of--freedom. For this type of systems transport in the chaotic regions of phase space is dominated by sticking to complicated structures in the vicinity of the regular region. The probability to stay in the vicinity of the initial point is a power law in time characterized by some exponent. The question of the value of this exponent and its universality is the subject of a long controversy. We have developed a statistical description for this type of systems, where statistics are with respect to parameter or family of systems rather than to initial conditions. Following previous studies, it is based on a scaling of periodic and quasi-periodic orbits in a way which relies heavily on number theory. We have found an indication that the statistics of scaling is parameter independent and might be relevant for a wider universality class including the systems we explored. This statistical description is implemented in a stochastic Markov model proposed by Meiss and Ott in 1986. Even though many approximations are used, it predicts important results quantitatively, showing the power law decay exponent to be approximately 51.57 in agreement with direct simulations done in this work and also other works. Its universality is inferred from the universality of the scaling statistics. The model systems used in this work are paradigms for chaotic dynamics (the H'enon map and the standard map) therefore it might indicate a wider universality class. Quantum manifestation of this phenomenon and its relevance for time correlations, is showing different behavior for increasing effective Planck's constant, namely, the Planck's constant divided by the typical action. By using recent results regarding the universality of wave function transmission across barriers in phase space, we generalize the use of the Markov model to describe the results after some modification. The work reported was done in collaboration with Or Alus, James Meiss and Mark Srednicki


September 27, 2018

Prediction of complex spatiotemporal evolution through machine learning methods improved with the addition of observers

Prof. George Tsironis

Department of Physics | University of Crete

Abstract: Can we use machine learning (ML) to predict the evolution of complex, chaotic systems? The recent Maryland-based work showed that the answer is conditionally affirmative once we use some additional “help” provided by a random bath and observers, as defined through reservoir computing (RC) [1]. What about using other “standard” ML methods in forecasting the future of complex systems? The ETH-MIT group showed that the long-short-term-memory (LSTM) method may work in general spatiotemporal evolution of the Kuramoto type [2]. Our work (Crete-Harvard) focused on the following question: Under what circumstances ML can predict spatiotemporal structures that emerge in complex evolution that involves nonlinearity as well as some form of stochasticity? To address this question we used two extreme phenomena, one being turbulent chimeras while the second involves stochastic branching. The former phenomenon generates partially coherent structures in highly nonlinear oscillators interacting through short or long range coupling while the latter appears in wave propagation in weakly disordered media. Examples of the former include biological networks, SQUIDs (superconducting quantum interference devices), coupled lasers, etc while the latter geophysical waves, electronic motion in a graphene surface and other similar wave propagation configurations. In our work we applied and compared three ML methods, viz. LSTM, RC as well as the standard Feed-Forward neural networks (FNNs) in the two extreme spatiotemporal phenomena dominated by coherence, i.e. chimeras, and stochasticity, i.e. branching, respectively [3]. In order to increase the predictability of the methods we augmented LSTM (and FNNs) with observers; specifically we assigned one LSTM network to each system node except for "observer" nodes which provide continual "ground truth" measurements as input; we refer to this method as "Observer LSTM" (OLSTM). We found that even a small number of observers greatly improves the data-driven (model-free) long-term forecasting capability of the LSTM networks and provide the framework for a consistent comparison between the RC and LSTM methods. We find that RC requires smaller training datasets than OLSTMs, but the latter requires fewer observers. Both methods are benchmarked against Feed-Forward neural networks (FNNs), also trained to make predictions with observers (OFNNs). [1] Z. Lu Z, J. Pathak, B. Hunt, M. Girvan, R. Brockett and E. Ott, Reservoir observers: Model free inference of unmeasured variables in chaotic systems. Chaos 27, 041102 (2017); J. Pathak, B. Hunt,M. Girvan, Z. Lu and E. Ott, Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach, Phys. Rev. Let. 120, 024102 (2018) [2] P. R. Vlachas, W. Byeon, Z. Y. Wan, T. P. Sapsis and P. Koumoutsakos, Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks. Proc.R.Soc.A 474, 20170844 (2018). [3] G. Neofotistos, M. Mattheakis, G. D. Barmparis, J. Hizanidis, G. P. Tsironis and E. Kaxiras, Machine learning with observers predicts complex spatiotemporal evolution, arXiv 1807.10758 (2018)


October 4, 2018

Due to unforeseen circumstances, we have a new speaker for this date

Climate model shows large-scale wind and solar farms in the Sahara increase rain and vegetation

Dr. Safa Motesharrei

University of Maryland | Department of Physics

Abstract: Wind and solar farms offer a major pathway to clean, renewable energies. However, these farms would significantly change land surface properties, and, if sufficiently large, the farms may lead to unintended climate consequences. In this study, we used a climate model with dynamic vegetation to show that large-scale installations of wind and solar farms covering the Sahara lead to a local temperature increase and more than a twofold precipitation increase, especially in the Sahel, through increased surface friction and reduced albedo. The resulting increase in vegetation further enhances precipitation, creating a positive albedo–precipitation–vegetation feedback that contributes ~80% of the precipitation increase for wind farms. This local enhancement is scale dependent and is particular to the Sahara, with small impacts in other deserts.


October 11, 2018

Constructing Chaotic Coordinates for non-integrable dynamical systems

Dr. Stuart Hudson

Princeton Plasma Physics Laboratory

Abstract: Action-angle coordinates can be constructed for so-called integrable Hamiltonian dynamical systems, for which there exists a foliation of phase space by surfaces that are invariant under the dynamical flow. Perturbations generally destroy integrability. However, we know that periodic orbits will survive, as will cantori, as will the "KAM" surfaces that have sufficiently irrational frequency, depending on the perturbation. There will also be irregular "chaotic" trajectories. By "fitting" the coordinates to the invariant structure that are robust to perturbation, action-angle coordinates may be generalized to non-integrable dynamical systems. These coordinates "capture" the invariant dynamics and neatly partition the chaotic regions. These so-called chaotic coordinates are based on a construction of almost-invariant surfaces known as ghost surfaces. The theoretical definition and numerical construction of ghost surfaces and chaotic coordinates will be described and illustrated.


October 18, 2018

Bifurcations in dynamical control systems for aerospace applications

Prof. Derek Paley

University of Maryland | Department of Aerospace Engineering

Abstract: This talk will discuss bifurcations in several dynamical control systems that arise in aerospace engineering applications. First, I will present the swimming dynamics and control of a flexible underwater robot based on closed-loop control of an internal reaction wheel. The feedback law stabilizes a limit cycle about the desired heading angle and produces forward swimming motion. Analysis of a global bifurcation in the dynamics under feedback control reveals the set of control gains that yields the desired limit cycle. Second, I will discuss a nonlinear control system consisting of a single vortex in a freestream near an actuated cylinder that represents an airfoil under a conformal mapping. Using heaving and/or surging of the cylinder as input stabilizes the vortex position relative to the cylinder. The closed-loop system utilizes a linear state-feedback control law, which gives rise to several bifurcations by varying the control gains. Lastly, time permitting, I will discuss a state-space model for representing the lift of an airfoil at high angles of attack. A feedback controller stabilizes a limit cycle in the angle of attack that provides greater (average) lift than a static pitch angle. In all three examples, incorporating dynamical systems theory complements the state-space modeling and control design.


October 25, 2018 - Room change: AV Williams 1147

A method for numerical computation starting from a quasiperiodic trajectory

Prof. Evelyn Sander

George Mason University | Department of Mathematics

Abstract: A trajectory is quasiperiodic if the trajectory lies on and is dense in some d-dimensional torus, and there is a choice of coordinates on the torus for which F has the form of a rigid rotation on the torus with rotation vector rho. There is an extensive literature on determining the rotation vector associate with F, as well finding Fourier components to establish these conjugacies. I will present two new methods with very good convergence rates: the Weighted Birkhoff Method and the Embedding Continuation Method. They are based on the Takens Embedding Theorem and the Birkhoff Ergodic Theorem. I will illustrate these for one- and two-dimensional examples ideas by computing rotation vectors or numbers, computing Fourier components for conjugacies, and distinguishing chaos versus quasiperiodic behavior.


November 1, 2018

Neuronal coding in the insect olfactory system

Prof. Quentin Gaudry

University of Maryland | Department of Biology

Abstract: The world is full of volatile chemical cues that animals must decipher to detect the presence of prey, predators, and even potential mates. The olfactory system is burdened with the task of interpreting a near infinite amount of odors given a limited repository of chemoreceptors. Studies emphasizing invertebrates have provided tremendous insight into the basic mechanisms of olfaction, and the highly analogous organization of invertebrate and mammal olfactory systems suggests that such studies can shed light upon how our own sense of smell functions. In this seminar, I will discuss data from Drosophila melanogaster and locusts revealing how olfactory information is transformed at subsequent stages of processing. Finally, I will discuss data from my own laboratory showing how neuromodulatory neurons that alter the sensory processing interact with the olfactory system.


November 8, 2018

Modeling methodologies for personal protection control strategies in vector-borne disease epidemiology: The role of diversity amplification

Dr. Jeff Demers

University of Maryland | Department of Biology

Abstract: Personal Protection measures, such as bed nets and personal repellents, are important tools for the suppression of vector-borne diseases like malaria and Zika, and the ability of health agencies to distribute protection and encourage its use plays an important role in the efficacy of community-wide disease management strategies. Recent modeling studies have shown that a counterintuitive diversity-driven amplification in community-wide disease levels can result from a population's partial adoption of personal protection measures, potentially to the detriment of disease management efforts. This finding, however, may overestimate the negative impact of partial personal protection as a result of implicit restrictive model assumptions regarding host compliance, access to, and longevity of protection measures. We establish a new modeling methodology for incorporating community-wide personal protection distribution programs in vector-borne disease systems which flexibly accounts for compliance, access, longevity, and control strategies by way of a flow between protected and unprotected populations. Our methodology yields large reductions in the severity and occurrence of amplification effects as compared to existing models.


November 15, 2018

Transiently Chaotic behavior in Superconducting Metamaterials

Amitava Banerjee

University of Maryland | Department of Physics

Abstract: In this seminar, we attempt to connect two of the major research vistas in nonlinear dynamics, namely, chimera states and chaos. We consider a simplified mathematical model of a one-dimensional lattice of coupled superconducting quantum interference devices (SQUIDs) driven by an external magnetic field [1,2]. We numerically simulate chimeras and other collective states in the magnetic flux oscillations through the SQUIDs and show that they are born through chaotic dynamics on finite time scales. We demonstrate the signatures of transient chaos in flux oscillations with fluctuating amplitudes, exponential escape time distribution, and fractal Wada basins of attraction for chimera states [1,3]. This study complements the identification of chimeras as transiently chaotic states themselves [4,5], and may be useful for prediction, characterization and control of such states.

References: 1. A. Banerjee and D. Sikder, Phys. Rev. E 98, 032220 (2018). 2. M. Trepanier, D. Zhang, O. Mukhanov, V. P. Koshelets, P. Jung, S. Butz, E. Ott, T. M. Antonsen, A. V. Ustinov, and S. M. Anlage, Phys. Rev. E 95, 050201(R) (2017). 3. Y.-C. Lai and T. Tel, Transient Chaos (Springer, New York, 2011). 4. M. Wolfrum and O. E. Omelchenko, Phys. Rev. E 84, 015201(R) (2011). 5. M. Wolfrum, O. E. Omelchenko, S. Yanchuk, and Y. L. Maistrenko, Chaos 21, 013112 (2011).


November 22, 2018

Thanksgiving Break - No seminar


November 29, 2018

Confining charged particle orbits using hidden symmetry

Dr. Matt Landreman

University of Maryland | IREAP

Abstract: Toroidal magnetic fields can confine charged particles, which can be exploited for basic physics studies or potentially for fusion energy. The magnetic field should lack axisymmetry (continuous rotational symmetry), or else a large electric current is needed inside the confinement region. However, the magnetic field should possess two properties that could be termed ‘hidden symmetries’. The first, integrability, means the field lines should lie on nested toroidal surfaces, without regions of islands or chaos. The second, called `quasi-symmetry’, generalizes the conservation of canonical angular momentum in the presence of strong magnetic fields. This second property arises because the Lagrangian for particle motion in strong magnetic fields can be expressed in terms of the strength of the field, independent of its direction. Magnetic fields with these properties can be found using optimization or using a new constructive procedure.


December 6, 2018

Invariant measures for the stochastic Navier-Stokes equations for compressible flows and the problem of Turbulence

Prof. Konstantina Trivisa

University of Maryland | Department of Mathematics and IPST

Abstract: Statistically stationary solutions to randomly forced systems have been of fundamental importance from both theoretical and practical points of view. From one hand the existence of invariant measures provides information on the long time dynamics of randomly forced systems and from the other, under certain ergodicity assumptions, it provides a link between experimental observations and theoretical predictions. In this talk I’ll present results on the long-time behavior of solutions to a stochastically forced one-dimensional Navier-Stokes system, describing the motion of a compressible viscous fluid. The existence of an invariant measure for the Markov process generated by strong solutions will be discussed.

February 9, 2017

Integrability of Matrix Riccati Equations that Arise in Hydrodynamics

David Levermore

University of Maryland | Department of Mathematics

Abstract: TBA


February 16, 2017

Plasmon Resonances in Nanoparticles and the Reimann Hypothesis

Isaac Mayorgoyz

University of Maryland | Department of Electrical and Computer Engineering

Abstract: TBA


February 23, 2017

Observation of Localizad Stress Fluctuations that Drive Shear Thinning in Dense Suspensions

Jeff Urbach

Georgetown University | Department of Physics

Abstract: TBA


March 2, 2017

Helicity Dynamics

Dan Lathrop 

University of Maryland | Department of Physics

Abstract: Helicity is a conserved quantity that arises in ideal fluid flows and ideal magnetohydrodynamic magnetic fields. I will first review the background theory of Helicity in those two cases, a famous paper by Finn and Antonsen, and another by Keith Moffatt. I will follow by covering some basic phenomenology of quantized vortices, reconnection, and Kelvin waves, and background of our visualization studies in superfluid helium. These topics lead into a discussion of what has been done, what we know, and what is predicted about Helicity dynamics. Some observations about the untangling of vortices via reconnection lead to predictions regarding the Helicity we are exploring experimentally. Some puzzles and questions about the role of invariants like the Helicity in the Gross-Pitaevskii (nonlinear Schrodinger) equation play a role in thinking about this phenomenon.


March 9, 2017

Toward a theory of reservoir computing prediction

Brian Hunt and Zhixin Lu

University of Maryland | Department of Mathematics and University of Maryland | IREAP

Abstract: We consider the problem of predicting a chaotic time series from a system whose equations of motion are unknown. We use a machine-learning technique called reservoir computing, which we find is often able to learn the dynamics of the system that generated the time series, in the following sense. In addition to making accurate short-term predictions, the reservoir predictor can generate a long-term "climate" forecast that stays close to the attractor of the actual system. We give examples, and we discuss a preliminary theory relating reservoir predictor performance to Lyapunov exponents and generalized synchronization in an associated dynamical system.


March 16, 2017

Flames, Fire Whirls, and Blue Whirls: What more can there be?

Elaine Oran

University of Maryland | Department of Aerospace Engineering

Abstract: As we were investigating the efficiency of fire-whirl burning on water, we observed the usual transformation of a pool fire to a fire whirl, and then suddenly, we saw the fire undergo a third transition. A blue cup appeared around the base of the fire whirl, surrounding the yellow flame, the yellow flame receded into the cup and finally disappeared. What remained was a small, rapidly spinning blue flame that burned until the fuel on the water was consumed. The blue whirl was shaped like a spinning cup, closed at the bottom near the water surface, and spreading in radius moving upwards towards the rim. Above the blue cup lip, there was a purple cone-shaped mist. The fuel initially used was n-heptane, but now it has been varied and includes crude oil, and still the blue whirl formed naturally. The height of the fire whirl on the laboratory pan was larger than a half meter, and this evolved into a blue whirl about 4–8 cm high. Occasionally the blue whirl would become “unstable” and revert to a transitional state of blue cup holding a yellow flame. When the blue whirl formed, turbulence seemed to disappear, and the flame became quiet. Videos of the experiments are used to show how this happened and discuss the evolution of the fire whirl to the blue whirl in vortex-breakdown concepts.


March 23, 2017 

Spring Break - No Seminar 


March 30, 2017 

Some Adjoint Methods in Physics and Engineering or How the solution to not my problem just might be the answer to your problem 

Thomas Antonsen

University of Maryland | Department of Physics

Abstract: Physicists and engineers frequently encounter situations where calculations of the governing equations of a system of interest appear to need to be repeated many times to describe or optimize the system. It is often the case that only a particular state dependent quantity or metric needs to be determined. In this case a computational savings can be achieved if an “adjoint problem” can be found that produces the desired information without requiring multiple computations. A simple example is the design of a receiving antenna. One wishes to know and optimize the signal received as a function of the incident angle and polarization of incoming waves. It might appear that solution of Maxwell’s field equations would have to be repeated for each possible incident direction and polarization. However, due to the reciprocal property of the governing equations, the desired information is obtained by treating the antenna as a transmitter and calculating the far field radiation pattern. Thus, one computation replaces many. In this talk I will review some problems from the area of charged particle dynamics where adjoint methods have proven useful. A new example is the optimization of electron beam optics in beam sources used in microwave and millimeter wave amplifiers.


April 6, 2017 

Why Have Network Modulation of Sensory Cortex that Causes Variability to Sensory Processing? 

Daniel Butts

University of Maryland | Department of Biology

Abstract: A fundamental goal in brain research is to understand how electrical activity of individual neurons represents information relevant for brain function. This is most often studied in sensory systems, where neural activity can be directly related to sensory stimuli that can be experimentally controlled. However, recordings in awake animals can reveal an enormous amount to variability — that is, different responses to the same stimuli. Such variability has traditionally been characterized as noise that imposes limits on sensory processing. However, with experimental technology allowing for access to large amounts of simultaneously recorded neurons, it is becoming clear that this noise is shared and purposeful, and likely relates to a larger view of the function of sensory cortex. My lab has been developing new methods for analyzing population activity (and its dynamics) to infer what information is being represented by this variability, and how it relates to the larger functions of sensory cortex. This points to a picture where sensory processing does not occur in a vacuum, but is implicitly tied to the behavioral and motivational context of the animal.


April 13, 2017 

Chaotic Dynamics in the Physical Sciences

Edward Ott

University of Maryland | Department of Electrical Engineering and Department of Physics

Ed Ott will be the recipient of the 2017 Lewis Fry Richardson Medal from The European Geosciences Union (E.G.U.). In connection with this award, he will give a lecture at the annual E.G.U. Assembly in Vienna, Austria, later this month. This Applied Dynamics Seminar will be a preview of his talk in Vienna.

Abstract: Chaos was discovered at the end of the 19th century by Poincare in his famous work on the motion of N>2 celestial bodies interacting through gravitational attraction. Although steady progress was made by mathematicians following Poincare's work, the widespread impact and development of chaos in the physical sciences is comparatively recent, i.e., approximately starting in the 1970's. This talk will review and comment on this history and will give some examples illustrating the types of questions, problems and results arising from perspectives resulting from the widespread participation of physical scientists in chaos research.


April 20, 2017 

Multi Chaos: A low dimensional Paradigm for higher-dimensional chaos

James Yorke

University of Maryland | Department of Mathematics

Abstract: The most frequently studied dynamical systems are low dimensional and all the periodic orbits in a chaotic set have the same number of unstable dimensions, but this property seems to fail in high dimensional systems. In this paper, we define a property called ``multi-chaos'', in which, along with the usual properties of chaos, there is a dense set of k-dimensionally unstable periodic orbits, and this holds for more than one k. We provide examples including a piecewise linear generalized Baker map. 


April 27, 2017 

Dynamics of Granular Clogging 

Doug Durian

University of Pennsylvania | Department of Physics

Abstract: The gravity-driven flow of grains from a hole in a hopper is an iconic granular phenomenon. It’s different from a fluid in that the rate is constant also in that it can suddenly and unexpectedly clog. How does the the susceptibility to clogging decrease with increasing hole size, and is there a well-defined clogging transition above which the system never clogs? This problem is distinct from jamming due to presence of boundaries and gradients. We show how the fraction F of flow configurations that cause a clog may be deduced from the average mass discharged between clogs. We construct a simple model to account for the observation that F decays exponentially in hole width to the power of dimensionality. Thus the clogging transition is not sharp but rather is defined by observation limits similar to the glass transition. When the system is immersed in water, F barely changes. Therefore, the crucial microscopic variables are the grain positions; grain momenta play only a secondary role in destabilizing weak incipient arches. There is also a surprising effect whereby the discharge causes water to be pumped downwards, faster than the grains. 


May 4, 2017 

Nonlinear Dynamics, Chaos and Complex Systems: a Historical Perspective

Miguel Sanjuán

Universidad Rey Juan Carlos | Department of Physics

Abstract: "When we talk about dynamics, we do not only understand the motion of celestial bodies and solid mechanical systems, but any changes with respect to time of one or more variables. From that point of view, we can find dynamics everywhere, in any field of science. Thus, now we have a more general vision, including stock market movements and economic variables, concentration changes in chemical reactions, changes in physiological, biological and medical variables, action potentials of neurons, etc ... providing a more interdisciplinary perspective. The various interactions between the constituent parts of a physical system and their feedback mechanisms, are a source of nonlinearity and complexity, which added to the sensitivity dependence to initial conditions which is a hallmark of chaotic behavior, constitutes a change of perspective in dynamical systems with important consequences for the understanding of science. I will give a historical perspective of Nonlinear Dynamics, Chaos Theory and Complex Systems, including some of the different sources that have contributed to the construction of the discipline as we know it today. Among them, the three-body problem in celestial mechanics, turbulence in fluid dynamics, irreversibility and fundamentals of statistical physics and the logistic map and population dynamics in biology. Many schools of mathematics and physics have played an essential role in the historical development of the subject, including the French, Russian, Japanese and American school. The knowledge of this historical perspective allows us to understand the breadth of the discipline itself and the multiple interdisciplinary applications to various fields of science. " 


May 11, 2017 

Non-Monotonic Aging and Memory Retention in Disordered Mechanical Systems

Yoav Lahini

Harvard University | Harvard John A. Paulson School of Engineering and Applied Sciences

Abstract: From materials such as polymers and glass to properties of interfaces leading to friction and even earthquakes, many disordered systems exhibit a similar repertoire of far-from-equilibrium behaviors such as non-exponential relaxations, aging and memory effects. Yet, in spite of numerous studies of these recurring motifs, identifying the mechanisms underlying the unusual dynamics of disordered systems remains a challenge. I will describe the observation of slow relaxations, aging and memory effects - hallmarks of glassy dynamics – in two disordered mechanical systems: crumpled thin sheets and elastic foams. In particular, I’ll report the observation of a non-monotonic aging response that can last many hours. I will then describe ongoing experiments that exploit the macroscopic nature of these systems to try and uncover the underlying mechanisms. The experimental results are in good agreement with a theoretical model recently used to describe observations of monotonic aging in several glassy systems. This suggests not only a general mechanism, but also that the non-monotonic behavior we observe may be generic and that a-thermal systems can show genuine glassy behavior. 


Special Seminar - Tuesday May 16 at 11am (ERF 1027), 2017 

Dynamics of Rewired Networks

Sudeshna Sinha

Indian Institute of Science Education and Research

Abstract: We will show how spatio-temporal chaos in networks with strongly chaotic nodal dynamics can be tamed by dynamically changing links. Specifically, we will illustrate the results in examples ranging from neuronal networks to disease spreading models. Further we will show how random links can prevent blow-ups in coupled nonlinear systems suffering from unbounded growth.


Special Seminar - May 18, 2017 

Topological methods for analyzing two dimensional flows

Tomoo Yokoyama

Kyoto University of Education | Department of Mathematics

Abstract: We introduce tree representations of two dimensional flows. Applying the topological methods to an evolution of an incompressible and viscid flow around an inclined flat plate placed in a uniform flow, we can estimate when the lift-to-drag ratios of the plate are maximal and can determine transient streamline patterns between structurally stable streamline patterns. Moreover, we state the possibilities of analyzing ocean phenomena and medical phenomena. Finally, we discuss low-dimensional dynamical systems which are theoretical backgrounds of the methods.


September 7, 2017

Ultrafast Large-scale Neural Network Processor on a Chip

Daniel Lathrop and Itamar Shani

University of Maryland | Department of Physics / IREAP

Abstract: Neural networks allow machines to imitate the way in which human intelligence solves problems by inferring from past experience. These networks are composed of large arrays of communicating neurons, each one performing a simple non-linear operation. When combined and trained by variation of connection weights, the network can perform complex perceptive computational tasks such as image and voice recognition and complex pattern predictions. When implementing neural networks on conventional digital processing hardware such as those at the core of our PCs, an immense inefficiency stands out: neural network computations are inherently parallel, while computers were designed to perform computations serially. This leads to slow computation times and a high toll of energy consumption. Here we report a way to overcome this challenge. We implement a silicon chip with thousands, and potentially millions, of processing interconnected ‘neurons’, each one operating at 200ps rates. The chip is, thus, capable of performing fully parallel, highly efficient computation. For the design of our network, we follow the well-established machine learning algorithms in which interconnections are described by a random sparse directed graph. We show preliminary laboratory measurements of the network dynamics on a chip and discuss its software variations.


September 14, 2017

Synchronization of Quantum Dipoles

Juan Restrepo

University of Colorado Boulder | Department of Applied Mathematics

Abstract: In this talk we discuss the emergence of synchronization in arrays of quantum radiating dipoles coupled only via anisotropic and long-range dipolar interactions. It is found that in the presence of an incoherent energy source, dipolar interactions can lead to a resilient synchronized steady-state. A classical mean-field description of the model results in equations similar to the classical Kuramoto model for synchronization of phase oscillators. Using the mean-field formulation for the all-to-all coupled case, the synchronized state can be studied, and it is found that it exists only for a finite range of the external energy source rates. Results obtained from the mean-field model are compared with numerical simulations of the quantum system and it is found that synchronization is robust to quantum fluctuations and spatially decaying coupling. Additional nonstationary synchronization patterns and bistability are discussed.


September 21, 2017

Basin Entropy: A Measure of the Final State Unpredictability and Applications to Some Physical Systems

Miguel Sanjuán

King Juan Carlos University | Department of Physics

Abstract: In nonlinear dynamics, basins of attraction link a given set of initial conditions to its corresponding final states. This notion appears in a broad range of applications where several outcomes are possible, which is a common situation in neuroscience, economy, astronomy, ecology and many other disciplines. Depending on the nature of the basins, prediction can be difficult even in systems that evolve under deterministic rules. From this respect, a proper classification of this unpredictability is clearly required. To address this issue, we introduce the basin entropy, a measure to quantify this uncertainty. Its application is illustrated with several paradigmatic examples that allow us to identify the ingredients that hinder the prediction of the final state. The basin entropy provides an efficient method to probe the behavior of a system when different parameters are varied.Additionally, we provide a sufficient condition for the existence of fractal basin boundaries: when the basin entropy of the boundaries is larger than log 2, the basin is fractal. These ideas have been applied to some physical systems such as experiments of chaotic scattering of cold atoms, models of shadows of binary black holes, and classical and relativistic chaotic scattering associated to the Hénon-Heiles Hamiltonian system in astrophysics.


September 28, 2017

A Model-Free Machine Learning Technique for Studying High Dimensional Spatiotemporal Chaos

Jaideep Pathak

University of Maryland | IREAP

Abstract: Networks of nonlinearly interacting neuron-like units have the capacity to approximately reproduce the dynamical behavior of a wide variety of dynamical systems. We demonstrate the use of such neural networks for reconstruction of chaotic attractors from limited time series data using a machine learning technique known as reservoir computing. The orbits of the reconstructed attractor can be used to obtain approximate estimates of the ergodic properties of the original system. As a specific example, we focus on the task of determining the Lyapunov exponents of a system from limited time series data. Using the example of the Kuramoto-Sivashinsky system, we show that this technique offers a robust estimate of a large number of Lyapunov exponents of a high dimensional spatiotemporal chaotic system. We further develop an effective, computationally parallelizable technique for model-free prediction of spatiotemporal chaotic systems of arbitrarily large spatial extent and dimension purely from observations of the system's past evolution.


October 5, 2017

Complexity and Self Organization in Superconducting Metamaterials

George Tsironis

University of Crete | Department of Physics

Abstract: Macroscopic quantum devices are becoming reality not only for computational purposes but also as sensors and for other general applications In this talk we will focus on superconducting technology and analyze the emergence of coherence in coupled networks of meta-atoms made of units such as SQUIDS and Josephson junctions. These networks may operate classically in a negative permeability regime[1], induce intrinsic nonlinear localized modes and partial coherence in the form of chimeras[2], tame disorder through hysteretic loops or transmit through nonlinear frequency bands. In the quantum regime, on the other hand, meta-atoms may interact through injected electromagnetic fields and form propagating “quantum breathers”, i.e. compound semi-classical propagating modes induced by the nonlinearity of the qubit-field interaction [3]. These coherent modes generate self-induced transparency in the medium and in certain cases may also induce super-radiance. [1] N. Lazarides and G. P. Tsironis, rf SQUID metamaterials, Appl. Phys. Lett. 90, 163501 (2007). [2] N. Lazarides, G. Neofotistos, and G. P. Tsironis, Chimeras in SQUID metamaterials, Physical Review B 91, 054303 (2015). [3] Z. Ivic, N. Lazarides, and G. P. Tsironis, Qubit lattice coherence induced by electromagnetic pulses in superconducting metamaterials, Scientific Reports 6, 29374(2016).


October 12, 2017

Granular Dynamics in Low Gravity

Derek Richardson

University of Maryland | Department of Astronomy

Abstract: Small solar system bodies are generally covered in layers of particulate "regolith" with largely unknown properties. The effective gravities on these bodies can sometimes tend to zero at the equator, and have been measured to be negative in a few cases. Space agencies and commercial enterprises show increasing interest in visiting, landing on, and sampling from such bodies, so it is important to understand how the regolith will respond to intrusion. Due to the difficulty and expense of carrying out experiments in low gravity, we turn to computer simulations of granular dynamics to provide insight into the conditions that missions to other worlds may encounter and to help interpret observations of these bodies. As high-end computing resources become more readily available, granular dynamics simulations have become more sophisticated, treating particle collisions as finite-duration, multi-contact events with explicit friction forces between irregularly shaped grains subject to external forces, boundary conditions, and cohesion. It is imperative that such simulation methods be calibrated and validated at laboratory scales to give confidence in their application to other environments. Here I review our group's approach to simulating granular dynamics in low gravity, with examples that include impacts into granular materials, vibration-induced segregation, landslides, models of samplers and landers, and, on larger scales, simulations of entire granular bodies (rubble piles) and planetary rings.


Special Seminar: FRIDAY October 13, 2017 at 3:00pm

Statistical Description of Mixed Systems (Chaotic and Regular), Correlations and "Thermalization"

Shmuel Fishman

Technion | Department of Physics

Abstract: We discuss a statistical theory for Hamiltonian dynamics with a mixed phase space, where in some parts of phase space the dynamics is chaotic while in other parts it is regular. Transport in phase space is dominated by sticking to complicated structures and its distribution is universal. The survival probability in the vicinity of the initial point is a power law in time with a universal exponent. We calculate this exponent in the framework of the Markov Tree model proposed by Meiss and Ott in 1986. It turns out that, inspite of many approximations, it predicts important results quantitatively. The calculations are extended to the quantum regime where correlation functions and observables are studied. The seminar will be very informal and some work still in progress will be reported. The work reported is in collaboration with Or Alus, James Meiss and Mark Srednicki.


October 19, 2017 

Ultra-High Intensity Laser Physics and Applications

Phillip Sprangle

University of Maryland | Department of Electrical and Computer Engineering / Department of Physics

Abstract: This talk will cover ultra-high field physics phenomena and interactions associated with high intensity short pulse lasers. The operating parameter regime of these lasers covers a wide range, e.g., peak powers of ~ 1012 - 1015 W, pulse lengths of ~ 10-12 – 10-14 sec, and intensities of ~ 1014 - 1023 W/cm2. These lasers are used in high-field physics research and have a number of unique applications. The physical processes associated with USPL interactions and propagation include: photo ionization, Kerr and Raman effects, self-phase modulation, optical and plasma filamentation, optical shocks, frequency chirping, propagation through atmospheric turbulence, etc. This talk will discuss these interrelated physical processes and some unique applications, such as: laser driven acceleration, UV/X-ray generation, underwater acoustic sources, atmospheric spark formation, detection of radioactive materials and atmospheric lasing.


October 26, 2017 

Neural Representation of Speech in Human Auditory Cortex

Jonathan Simon

University of Maryland | Department of Electrical and Computer Engineering

Abstract: We investigate how continuous speech is represented in human auditory cortex. We use magnetoencephalography (MEG) to record the neural responses of listeners to natural, continuous speech, in a variety of auditory scenes. Systems analysis, which quantitatively compares a speech signal to its evoked cortical responses, allows us to determine the cortical representations of the speech. Interestingly, the cortical representation allows the time-varying envelope of the speech to be reconstructed from the observed neural response to the speech. We find that cortical representations of continuous speech are very robust to interference from competing speakers, and many other kinds of noise, consistent with our ability to understand speech even in a noisy room (the "Cocktail Party" problem). Indeed, individual neural representations of the speech of both the foreground and background speaker are observed, with each being selectively time-locked to the rhythm of the corresponding speech, but the with the foreground speech represented more faithfully than the background.


November 2, 2017 

Modeling the Network Dynamics of Pulse-Coupled Neurons

Sarthak Chandra

University of Maryland | IREAP

Abstract: Computer modeling of neural dynamics is an important component of the long-term goal of understanding the brain. A barrier to such modeling is the practical limit on computer resources given the enormous number of neurons in the human brain (about 10^11.) Our work addresses this problem by developing a method for obtaining low dimensional macroscopic descriptions for functional groups consisting of many neurons. Specifically, we formulate a mean-field approximation to investigate macroscopic network effects on the dynamics of large systems of pulse-coupled neurons and derive a reduced system of ordinary differential equations describing the dynamics. We find that solutions of the reduced system agree with those of the full network. This dimensional reduction allows for more efficient characterization of system phase transitions and attractors. Our results show the utility of these dimensional reduction techniques for analyzing the effects of network topology on macroscopic behavior in neuronal networks.


November 9, 2017 

Optimal Bounds and Extremal Trajectories for Time Averages in Dynamical Systems

Charles Doering

University of Michigan | Mathematics / Center for the Study of Complex Systems

Abstract: For any quantity of interest in a dynamical system governed by ordinary differential equations it is natural to seek the largest (or smallest) long-time average among solution trajectories. Upper bounds can be proved a priori using auxiliary functions, the optimal choice of which is a convex optimization. The problems of finding maximal trajectories and minimal auxiliary functions are in fact strongly dual so auxiliary functions can produce arbitrarily sharp upper bounds on maximal time averages. They also define volumes in phase space where maximal trajectories must lie. For polynomial equations of motion auxiliary functions can be constructed by semidefinite programming, which we illustrate using the Lorenz system. This is joint work with Ian Tobasco and David Goluskin.


Special Seminar: TUESDAY November 14, 2017 at 11:00am

Sturm-Liouville Framework for Dynamical Reconstruction by Delay Embedding

Naoto Nakano

Kyoto University | Department of Mathematics

Abstract: Delay embedding is well-known for non-linear time-series analysis, and it is used in several research fields such as physics, informatics, neuroscience and so on. The celebrated theorem of Takens ensures validity of the delay embedding analysis: embedded data preserves topological properties, which the original dynamics possesses, if one embeds into some phase space with sufficiently high dimension. This means that, for example, an attractor can be reconstructed by the delay coordinate system topologically. However, configuration of an embedded dataset may easily vary with the delay width and the delay dimension, namely, ``the way of embedding". In a practical sense, this sensitivity may cause degradation of reliability of the method, therefore it is natural to require robustness of the result obtained by the embedding method in certain sense. In this study, we investigate the mathematical structure of the framework of delay-embedding analysis to provide Ansatz to choose the appropriate way of embedding, in order to utilize for time-series prediction. In short, mathematical theories of the Hilbert-Schmidt integral operator and the corresponding Sturm-Liouville eigenvalue problem underlie the framework. Using these mathematical theories, one can derive error estimates of mode decomposition obtained by the present method and can obtain the phase-space reconstruction by using the leading modes of the decomposition. In this talk, we will show some results for some numerical and experimental datasets to validate the present method.


November 16, 2017 

Adaptive Coding for Sensory Inference in Dynamic Environments

Ann Hermundstad

HHMI Janelia Research Campus

Abstract: Making reliable inferences about the environment is crucial for survival. In order to escape a hawk, for example, a mouse might need to infer the hawk’s position and velocity from patterns of light that fall on its retina. Such inferences require large ensembles of sensory neurons whose activity is metabolically expensive. A growing body of evidence suggests that sensory systems reduce metabolic costs by limiting the fidelity with which some stimuli are encoded in neural responses. Limited coding fidelity, however, can lead to inaccuracies in inference. Here, we derive a framework for dynamically balancing the cost of encoding with the error that encoding can induce in inference. We model a system that must use minimal metabolic resources to maintain an accurate estimate of a nonstationary environment, and we show that the optimal system should adapt the fidelity with which stimuli are encoded in neural responses based on a changing estimate of the environment. We use this framework to illustrate how a range of neuronal and behavioral phenomena can be understood as signatures of adaptive encoding for accurate inference.


November 23, 2017 

Thanksgiving Break - No Seminar 


November 30, 2017 

No Equations, No Variables, No Parameters, No Space, No Time: Data and the Modeling of Complex Systems

Yannis Kevrekidis

Johns Hopkins Univeristy | Department Chemical and Biomolecular Engineering / Department of Applied Mathematics and Statistics

Abstract: Obtaining predictive dynamical equations from data lies at the heart of science and engineering modeling, and is the linchpin of our technology. In mathematical modeling one typically progresses from observations of the world (and some serious thinking!) first to equations for a model, and then to the analysis of the model to make predictions. Good mathematical models give good predictions (and inaccurate ones do not) - but the computational tools for analyzing them are the same: algorithms that are typically based on closed form equations. While the skeleton of the process remains the same, today we witness the development of mathematical techniques that operate directly on observations -data-, and appear to circumvent the serious thinking that goes into selecting variables and parameters and deriving accurate equations. The process then may appear to the user a little like making predictions by "looking in a crystal ball". Yet the "serious thinking" is still there and uses the same -and some new- mathematics: it goes into building algorithms that "jump directly" from data to the analysis of the model (which is now not available in closed form) so as to make predictions. Our work here presents a couple of efforts that illustrate this ``new” path from data to predictions. It really is the same old path, but it is travelled by new means.


December 7, 2017 

Optical Control of Excitation Waves in A Biological Excitable Medium

Gil Bub

McGill University | Department of Physiology

Abstract: Macroscopic excitation waves are found in a diverse range of settings including chemical reactions and the heart and brain. In the case of living biological tissue, the spatiotemporal patterns formed by these excitation waves are different in healthy and diseased states. Current electrical and pharmacological methods for wave modulation lack the spatiotemporal precision needed to control these patterns. Optical methods have the potential to overcome these limitations, but until recently have only been demonstrated in simple systems. Here I discuss results using a new dye-free optical imaging modality with optogenetic actuation to achieve dynamic control of cardiac excitation waves. Illumination with patterned light is demonstrated to optically control the direction, speed, and spiral chirality of such waves in cardiac tissue. This all-optical approach offers a new experimental platform for the study and control of pattern formation in complex biological excitable systems.

February 4, 2016

Nonlinear Dynamics of a Plucked String

Dan Lathrop and Ed Ott

University of Maryland, IREAP

February 11, 2016

Scattering Theory for the Boltzmann Equation and the Arrow of Time

David Levermore

University of Maryland, Dept of Mathematics

Abstract: We develop a scattering theory for a class of eternal solutions of the Boltzmann equation posed over all space. In three spatial dimensions, each of these solutions has thirteen conserved qualities. The Boltzmann entropy has a unique minimizer with the same thirteen conserved values. This minimizer is a local Maxwellian that is also a global soution of the Botlzmann equation - a so-called global Maxwellian. We show that each of our external solutions has a streaming asymptotic state as time goes to minus- or plus-infinity. However, it does not converge to the associated gloabl Maxwellian as time goes to infinity unless it is that global Maxwellian. The Blotzmann entropy decreases as time increases, but does not decrease to its minimum as time goes to infinity. Said another way, the final step in the traditional argument for the heat death of the universe is not valid. 

February 18, 2016

Particle laden flows

Andrea Bertozzi

UCLA, Dept of Mathematics

Abstract: Modeling of particle laden flow, especially in the case of higher particle concentrations, does not readily allow for first principles models. Rather, semi-empirical models of the bulk dynamics require careful comparison with experiments.  At UCLA we have developed this theory for the geometry of viscous thin film flow with non-neutrally buoyant particles.  We have found that for these slower flows, that diffusive flux models, involving a balance between shear-induced migration and hindered settling, can provide reasonably accurate predictive models.  I will discuss the current state of this work including recent extensions to bidensity slurries and the relevant mathematics needed to understand the dynamics.  Lubrication theory can be derived for this problem and results in a coupled system of conservation laws including regular shock dynamics and singular shocks. I will also briefly discuss relevant applications such as spiral separators.

February 25, 2016

Titles Below

Jeffery Demers and Ayoti Patra 

University of Maryland, IPST/Dept of Chemistry

Jeff Demers

Universal energy diffusion in a quivering billiard

Abstract:  In this talk, I will discuss a particular limit of time-dependent billiard motion called "the quivering limit," and the resulting billiard systems called "quivering billiards."  I will show that the quivering limit is well-defined and physically interesting, yet allows for analytic calculations of physical quantities such as correlations and time-dependent energy distributions.  As time allows, I will share some interesting and surprising features of quivering billiards; namely that time-dependent billiards behave universally in the quivering limit, regardless of billiard shape or dimensionality, and that the insights gained through studying quivering billiards resolve some long standing problems in the time-dependent billiard literature.  I will also mention some exciting ongoing projects which employ quivering billiards to address current topics of interest in non-equilibrium thermodynamics and statistical mechanics at the nanoscale.

Ayoti Patra

Shortcuts to adiabaticity for a quantum tilted piston

Abstract: Shortcuts to Adiabaticity are techniques used to control a system evolving under a rapidly changing Hamiltonian. I will start by describing one such technique, known as 'Transitionless Quantum Driving', in which a quantum system is subjected to evolve under a composite Hamiltonian H_0(t) + H_{CD}(t), such that it remains in a given energy eigenstate of H_0(t) throughout the evolution. I will describe a method to obtain the term H_{CD}(t) starting from an analogous problem of 'classical dissipationless driving'. I will illustrate this method using the example of a tilted piston.

March 3, 2016

Nonlinear Dynamics of SQUID Metamaterials

Steve Anlage

University of Maryland, Depts of Physics and Electrical & Computer Engineering 

March 10, 2016

Many body physics of disordered microfluidic droplet ensembles

Itamar Shani

University of Maryland, IREAP

Abstract: Non-equilibrium systems with long-range interactions that exhibit complex collective dynamics are common in Nature, for example stellar motion, electric conduction, sedimentation, and plasma. However, understanding collective motion from basic principles is challenging because these systems are not governed by global laws such as energy minimization, and since every constituent interacts with many others. I will present experimental observations and theory of the dynamics of microfluidic droplet ensembles driven in a two-dimensional channel and governed by long-range hydrodynamic dipolar interactions. While the ensemble is spatially disordered, the droplet velocities exhibit long-range correlations proportional to 1/r2 with four-fold angular symmetry. Scatterings between three droplets at a time can break the long-range order by creating a large scale motion of contraction-expansion that dominates velocity correlations. We discovered that three-body collisions are also the fundamental element of irreversible dynamics in the reversible Stokes flow regime. The low-dimensionality of the system and the linearity of the flow equations facilitate a microscopic description of the rich collective behavior. 

March 17, 2016 

Spring Break - No Seminar 

March 24, 2016 

Chimera states/cluster synchronization 

Joe Hart and Kanika Bansal

University of Maryland, IREAP 

March 31, 2016 

Shortcuts to adiabaticity in simple classical systems 

Chris Jarzynski 

University of Maryland, Department of Chemistry and Biochemistry 

Abstract: Adiabatic invariants are important in classical mechanics, quantum mechanics and thermodynamics. I will consider the following classical problem in one degree of freedom. Given a time dependent Hamiltonian H(q,p,t), we wish to construct an auxiliary potential U(q,t) with the property that all trajectories launched from a specified initial energy shell E_0 of H(q,p,0), and subsequently evolving under H(q,p,t) + U(q,t), will end on a single energy shell E_tau of H(q,p,tau). By Liouville’s theorem these two shells share the same action. In this manner the auxiliary potential U(q,t) steers the trajectories so that the final action is exactly the same as the initial action, for every trajectory and for arbitrarily fast time-dependence of H(q,p,t). I will present a simple solution to this problem and will discuss its relationship to analogous solutions for quantum systems.

April 7, 2016

Glial Network Regularization of Neuron Learning Dynamics

Juan Restrepo 

University of Colorado, Department of Applied Mathematics

April 14, 2016

Measurement inspired modeling of quantum and classical dynamical systems

Denys Bondar

Princeton University, Department of Chemistry

Abstract: In this talk, I will provide an answer to the question: “What kind of observations and assumptions are minimally needed to formulate a physical theory?” Our answer to this question leads to the new systematic approach of Operational Dynamical Modeling (ODM), which allows to deduce equations of motions from time evolution of observables. Using ODM, we are not only able to re-derive well-known physical theories (such as the Schrodinger and classical Liouville equations), but also infer novel physical dynamics (and solve open problems) in the realm of non-equilibrium quantum statistical mechanics.

April 21, 2016 

Synchronization in Populations of Chemical Oscillators: Quorum Sensing, Phase Clusters, and Chimeras

Ken Showalter 

West Virginia University, Department of Chemistry

Abstract: We have studied large, heterogeneous populations of discrete chemical oscillators (~100,000) to characterize two different types of density-dependent transitions to synchronized behavior, a gradual Kuramoto synchronization and a sudden quorum sensing synchronization. We also describe the formation of phase clusters, where each cluster has the same frequency but is phase shifted with respect to other clusters, giving rise to a global signal that is more complex than that of the individual oscillators. Finally, we describe experimental and modeling studies of chimera states and their relation to other synchronization states in populations of coupled chemical oscillators.

April 28, 2016

Nonlinear Dynamics and Variational Statistical Data Assimilation

Henry Abarbanel 

UC San Diego, Department of Physics 

Abstract: This is about how to do 4DVar, as the meteorologists call it--with some confidence one has found the lowest minimum for the cost function--and, methods for evaluating the corrections to this approximation. Many more subtle details which I would be pleased to get your views about as well.

May 5, 2016 

Irregular spiking of pyramidal neurons organizes as scale-invariant neuronal avalanches in the awake state 

Dietmar Plenz

NIH, National Institute of Mental Health

May 12, 2016

X-ray Binary Light Curves: Can Data Analysis Lead to a Dynamical Model of Long Term Variability? 

Padi Boyd 

NASA, Goddard Space Flight Center

Abstract: The Time Domain Astronomy (TDA) renaissance is already well underway. The K2 mission is amassing an impressive collection of high precision, evenly sampled, long time baseline optical observations on par with those from the original Kepler mission, and high-energy space-based all-sky monitors continue to watch virtually every bright X-ray source in the sky.   These facilities monitor accreting compact binaries: some show high-amplitude “superorbital” variability, on timescales longer than the orbital timescale, sometimes ascribed to the motion of a warped, precessing accretion disk.  Warped accretion disks appear to be important in many astrophysical environments, from planet formation to accreting supermassive black holes at the hearts of active galaxies. But even the most well-behaved X-ray binary sources show surprising deviations from strictly periodic variability.  This talk will explore the variety of non-periodic variations seen in these systems, and some of the analysis tools used beyond traditional power spectral analysis, which is often insufficient. Such tools include phase-space embedding and topological analysis used by some in the nonlinear dynamics community. As the time baseline grows, these tools become more powerful, giving us hints about what drives such variability. I hope to get you thinking and commenting about whether a recently proposed accretion disk dynamo model might be able to explain the double-well-potential-like behavior seen in some systems; thus taking the next steps in understanding the dynamics of accretion disks.


September 8, 2016

Perception and Memory in Animal Movement

Bill Fagan

University of Maryland | Department of Biology

Abstract: Individual animals acquire information from their surroundings, gain and store knowledge from experience, and use what they have learned to navigate through dynamic landscapes. I will first discuss a mathematical model of animal perception in dynamic landscapes (this is formulated as a partial integro-differential equation featuring non-local advection) and then present some biological results that frame challenges in the modeling of spatial memory.


September 15, 2016

On the Generation of Gravity-Capillary Solitary Waves in Deep Water

Naeem Masnadi

University of Maryland | Department of Mechanical Engineering

Abstract: Nonlinear solitary waves are known to bifurcate from linear sinusoidal waves in deep water at the minimum phase speed of linear gravity-capillary waves (c_min=23 cm/s). This minimum happens at a finite wavenumber and the solitary waves can be viewed as modulated wavepackets with the wave envelope moving at the same speed as the wave crests. The minimum phase speed is also associated with a resonant condition; the linear response to a surface pressure distribution moving at this speed becomes unbounded. The nonlinear response offers rich and complicated dynamics and several solution regimes are found due to a delicate interplay between the effects of nonlinearity, dispersion and viscous dissipation. I will first review some theoretical background and modeling approaches to the problem of the response of a water surface to a pressure source moving at speeds close to c_min and then present our recent experimental and numerical findings..


September 22, 2016

The Markovian Mpemba Effect

Oren Raz

University of Maryland | Department of Chemistry and Biochemistry

Abstract: Under certain conditions, it takes less time to cool a hot system than to cool the same system initiated at a lower temperature. This phenomenon – the “Mpemba Effect”, has been observed in a variety of systems, including water, magnetic alloys and carbon nano-tube resonators. So far, no single generic mechanism to explain this counter-intuitive behavior was suggested. Using the theoretical framework of nonequilibrium thermodynamics, we construct a minimal model that describes this behavior and illustrates the fundamental principles behind it. We derive a sufficient condition for this effect in Markovian systems, and predict an inverse effect: it might take less time to heat a cold system than a warmer one.


September 29, 2016

Practical Kalman Filtering with and Without a Model

Tyrus Berry 

George Mason University | Department of Mathematical Sciences


October 6, 2016

The Force of Fluctuations: Analysis and Control of Extinctions on Networks

Ira Schwartz

Naval Research Laboratory

Abstract: Noise in various forms is known to cause switching between states, create new meta-stable states, and form global dynamical structures. In this talk, I will review some previous work on the effects of noise in static and adaptive networks, and show how to extend the theory to heterogeneous networks with a specified degree distribution. Applications will be to epidemic spread and novel optimal network control in large populations. Specifically, we have developed new mathematical and computational techniques demonstrating that both highly connected individuals and those with a few connections should be targeted in specific proportions, using vaccination or treatment, in order to minimize mean extinction times. The optimal approach gives orders of magnitude improvement over known strategies.


October 13, 2016

Noise-Influenced Dynamics

Balakumar Balachandran

University of Maryland | Department of Mechanical Engineering 


October 20, 2016 

Chaos, Noise and Random Numbers 

Joe Hart and Rajarshi Roy

University of Maryland | Department of Physics, IREAP, IPST


October 27, 2016 

European Neolithic societies (8000-4000 BP) exhibited early warning signs in advance of dramatic social collapse: New evidence from modeling and computational statistics

Sean Downey

University of Maryland | Anthropology Department

Abstract: This study uses statistical tests known as "early warning signals" (EWS) to determine whether declining socio-ecological resilience presaged a pattern of collapse during the Early Neolithic Period in Europe. Our earlier research has shown with a high degree of certainty that radiocarbon-inferred human demography during the Neolithic exhibits a boom-and-bust pattern. In this new study we analyze our meta-database of radiocarbon dates in order to determine whether societies on the verge of major reorganization—regime shift— may exhibit declining resilience, and if it can be detected using statistical methods. In seven of nine regional datasets we find increasing autocorrelation and variance leading up to collapse, suggesting that these societies began to recover from perturbation more slowly as resilience declined. We use simulation to validate our methods and show that sampling biases, atmospheric effects, radiocarbon calibration error, and taphonomic processes are unlikely to explain the observed EWS patterns. While EWS have been detected in biology and ecology, to our knowledge, this study is the first to find early warning signals of demographic regime shift among human populations.


November 3, 2016 

Reservoir Computing Using Autonomous Boolean Networks

Dan Gauthier

Ohio State University | Department of Physics

Abstract: Reservoir computing is a new approach to machine learning that uses a dynamical system for processing information. The dynamical system is a recurrent network where the weights of the network links are randomly assigned, as well as the weights for feeding information into the network. Only the output weights are adjusted to optimize the performance of the reservoir computer for a particular task, which is a linearly optimization problem and hence can be done quickly. I will describe the basic properties of reservoir computers, how they can be realized efficiently in hardware on a field-programmable gate array, and their application to a written-digit classification task and the forecasting of a chaotic dynamical system. 


November 10, 2016 

Spatio-Temporal Optical Vortices 

Howard Milchberg

University of Maryland | IPST, Department of Electrical and Computer Engineering, Department of Physics

Abstract: When an optical pulse propagating through a nonlinear medium exceeds a certain threshold power, it can focus itself and collapse, in theory, to a singularity. In practice, several physical mechanisms mitigate or arrest the catastrophic collapse and the pulse continues propagation as a filamentary structure. This scenario has played out in many nonlinear optics systems over decades: among them are air filamentation, relativistic self-focusing in plasmas, nonlinear generation of broadband light, laser-material processing, and unintentional (and expensive) laser damage. Recently, we showed that self-focusing collapse and collapse arrest is universally accompanied by the generation of robust topological structures: spatio-temporal optical vortices (STOVs).  In contrast to the conventional orbital angular momentum vortices in, for example, Laguerre-Gaussian beams, which can be described purely by spatial coordinates, STOVs have electromagnetic phase circulation in a spatio-temporal plane that propagates with the pulse and directs the global pulse energy flow.  I will describe our experiments, simulations and calculations leading to our discovery of STOVs , and discuss future possible applications. 


November 17, 2016 

ECHO, ECHo, Echo, echo... When Echoes Overwhelm Landau Damping

William Dorland

University of Maryland | Department of Physics, IREAP

Abstract: The Liouville equation describing a collection of charged particles is time-reversible. In the weakly coupled limit, one can reduce this equation to a Fokker-Planck equation, which is irreversible. The problem of the fate of electromagnetic field fluctuations in a plasma in the limit of very weak irreversibility was addressed by Landau, who demonstrated that as long as there are some collisions (even if rare), and in the absence of sources, gradients, etc, typical field fluctuations are damped with an easily calculated "collisionless" damping rate -- this is Landau damping. The energy of the field fluctuations is converted to particle energy; there is irreversible heating. Landau's calculation is fine in the limit of small amplitude fluctuations, but what happens when the plasma is turbulent? I will show that in a typical nonlinear system (relevant to many physical observations), Landau damping is overwhelmed and ultimately arrested by turbulent "echoes". This finding has important implications for detailed predictions of the heating (and in some cases, for the luminosity) of some interesting astrophysical plasmas.


November 24, 2016 

Thanksgiving Break - No Seminar 


December 1, 2016 

A book report: How complex cells evolved from bacteria, a story of energy 

James Yorke

University of Maryland | Department of Mathematics


December 8, 2016 

Fermi Acceleration in Discontinuous Systems  

Dmitry Doglopyat 

University of Maryland, Department of Mathematics 

September 10, 2015

When the Moon stays where it belongs: An introduction to quasiperiodicity
Prof. James Yorke

University of Maryland Distinguished Research Professor of Mathematics and Physics


September 17, 2015

What’s in a label? Language matrices and their dynamical interactions
Prof. Juan Uriagereka

University of Maryland, Dept of Linguistics

Abstract: Formal and generative linguistics has always assumed the manipulation of feature matrices in its operations. However, relatively little thought had been given, until the last decade or so, to how these putative matrices operate, beyond the assumption that they somehow do. This talk explicitly argues that matrix multiplication (Hadamard and Kronecker products) is a useful way to operate with linguistic matrices. Under standard linguistic assumptions (in particular "projection", the idea that a linguistic phrase of type X must immediately dominate another syntactic object of type X – e.g. a verb phrase contains a verb, a noun phrase contains a noun, etc.), this view of things predicts a number of distributional facts among phrasal dependents, which currently is not predicted in any other way. The formalism also has significant consequences for long-distance relations among phrases in syntax (e.g. conditions on question formation, ellipsis, etc.) and the study of grounding natural language on the mind/brain of speakers.


September 24, 2015

Optical and optomechanical response of visible frequency plasmonic metamaterials
Dr. Amit Agrawal

UMD, NIST-Nanocenter


October 1, 2015

A Low Paradigm for High Dimensional Chaos
S. Das

University of Maryland, Dept of Mathematics


October 8, 2015

The evolution and complexity of self-assembling structures in biology
Dr. Sebastian Ahnert

Cambridge University, Cavendish Lab


October 15, 2015

From oscillating candles to complex spatiotemporal arrhythmias in the heart
Prof. Flavio Fenton

Georgia Tech, School of Physics


October, 22, 2015

Electrostatic levitation of dust grains near the moon and asteroids
Prof. Christine Hartzell

University of Maryland, Department of Aerospace Engineering


October 29, 2015

Pressure driven flow of normal and super fluids through single nanopipes
Prof. Peter Taborek

University of California - Irvine, Department of Physics and Astronomy

Abstract: No slip boundary conditions successfully describe macroscopic flows near solids, but this boundary condition is not a law of physics, and several recent experiments have invoked large slip lengths to explain surprising results on flow through carbon nanotubes and other nanoscale flows. We will describe measurements of the mass flow rate as a function of pressure for flow through nanopipes made from single ion etch tracks 20 nm in diameter and glass tubes 200 nm in diameter. The extremely small flows involved are detected with a mass spectrometer. For classical fluids, our measurements always yield slip lengths less than 2 nm. For superfluid 4He , the flow rate has a complicated dependence on temperature and pressure which reflects the kinetics of vortex nucleation and vortex motion in the nanopipe.


November 5, 2015

Core-shell microgels: smart materials with tunable properties
Dr. Manis Chaudhuri

Harvard University 


November 12, 2015

Quantifying stability in networks: From linear to basin stability
Juergen Kurths

Potsdam Inst. for Climate Impact and Humboldt University


November 19, 2015

Insects in a Changing World: Modeling Insect Populations In the Context of Climate and Land-Use Change
Dr. Sharon Bewick

University of Maryland, Department of Biology


November 26, 2015



December 3, 2015

Wave chaos and enhancement of coherence with rippled waveguides
Dr. Dong Ho Wu

Naval Research Laboratory


December 10, 2015

The Spectrum of Wind Power Fluctuations
Mahesh Bandi

Okinawa Institute of Science and Technology

Abstract: Conventional energy sources such as nuclear or coal generate energy at a constant rate. Renewables on the other hand fluctuate with the variability in the natural sources from which they derive energy. Such fluctuations are particularly acute for wind and solar photovoltaics. On one hand, such fluctuations threaten the stability of the grid, whereas on the other, matching the fluctuating power production with a variable consumer demand presents scheduling difficulties for grid operators. Understanding fluctuations in renewables is also important for the design of robust smart grid technologies for the future. 

In this talk, I will chart out the non-equilibrium character of wind power fluctuations which depend upon the turbulent wind blowing past the wind turbines. Indeed, the spectrum of wind power fluctuations is widely known to reflect the Kolmogorov spectrum of turbulence; both vary with frequency $f$ as $f^{-5/3}$. Yet it has not been possible to derive this spectrum from the turbine power equation which relates the generated power $P$ to the wind speed $v$. I will explain the wind power fluctuation spectrum and show it arises from the violation of an underlying assumption in Kolmgorov theory of 1941 with crucial implications for wind power. In particular, every individual turbine feels the influence of the largest length scales of atmospheric turbulence. As a result, turbines within and between wind farms become coupled with each other at low turbulent frequencies over large distances. Consequently, when geographically distributed wind farms feed their power to the electrical grid, the fluctuations remain correlated and smooth out until they reach a theoretical bound that can be deduced from Kolmogorov theory. I will close my talk with a summary of engineering and policy implications of these results.