Nonparametric inference of interaction laws in systems of agents from trajectory data

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Authors Mauro Maggioni, Ming Zhong, Sui Tang, Fei Lu
Journal/Conference Name Proceedings of the National Academy of Sciences of the United States of America
Paper Category
Paper Abstract Inferring the laws of interaction between particles and agents in complex dynamical systems from observational data is a fundamental challenge in a wide variety of disciplines. 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 by testing the approach on a variety of prototypical systems in various disciplines. These systems include homogeneous and heterogeneous agents systems, ranging from particle systems in fundamental physics to agent-based systems modeling opinion dynamics under the social influence, prey-predator dynamics, flocking and swarming, and phototaxis in cell dynamics.
Date of publication 2018
Code Programming Language MATLAB
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