Event
Applied Dynamics Seminar| Jaideep Pathak, University of Maryland
Thursday, September 28, 2017
12:00 p.m.
ERF 1207
Taylor Prendergast
301 405 4951
tprender@umd.edu
Speaker: Jaideep Pathak
Speaker's Institution: University of Maryland | IREAP
Title: A Model-Free Machine Learning Technique for Studying High Dimensional Spatiotemporal Chaos
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.
