Recognizing Emergent Behaviors from Short-time Trajectory Data
Dr. Ming Zhong, Department of Applied Mathematics & Statistics, Johns Hopkins University
The study of emergent behaviors in collective dynamics is a fundamental challenge in a wide variety of disciplines. Classical approaches focus mainly on inducing the emergent behaviors from known interaction laws. We, on the other hand, developed a nonparametric inference approach to learn the interaction laws from trajectory data in [1], and use it to recover the desired dynamics. Having theoretically and computationally examined the convergence properties of our estimators in [1], we employ them to predict the corresponding emergent behaviors. We investigate the prediction capability of our estimators by testing them on a wide range of collective dynamics: opinion dynamics, flocking dynamics, milling dynamics, synchronized oscillator dynamics, and planetary motion in our Solar system.
[1]: F. Lu, M. Zhong, S. Tang, and M. Maggioni, Nonparametric inference of interaction laws in systems of agents from trajectory data, Proceedings of the National Academy of Sciences, 116 (29), 14424 - 14433, 2019.
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