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Applied Dynamics Seminar Series

As of March 25, all remaining Spring 2020 Applied Dynamics seminars have been cancelled.


Applied Dynamics Seminar Series


Thursdays, 12:30 p.m.


IREAP Large Conference Room (ERF 1207)

The organizers of the Applied Dynamics seminar gratefully acknowledge support from

the Institute for Research in Electronics and Applied Physics and the Institute for Physical Science and Technology.

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As of March 25, all remaining Spring 2020 Applied Dynamics seminars have been cancelled.

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

[CANCELLED] 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.

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