Event Start
Event Time
2:00 pm
4122 CSIC Building

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.

Event Start
Fall 2019