Speaker: Sui Tang, JHU
Title: Learning interaction laws in high dimensional agent-based dynamics from observations

Abstract: In different disciplines, inferring the laws of interaction of agents in complex dynamical systems from observational data is a fundamental challenge. We propose a non-parametric statistical learning approach to estimate the governing laws of distance-based interactions, with no reference or assumption about their analytical form, from data consisting trajectories of interacting agents. We demonstrate the effectiveness of our learning approach both by providing theoretical guarantees, and numerical tests on several prototypical systems.

Time: Friday, November 9, 2018, 1:30-2:30pm

Place: Exploratory Hall, Room 4106

Department of Mathematical Sciences
George Mason University
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Fairfax, VA 22030-4444
http://math.gmu.edu/
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