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DPS Online Seminar: John Baras, "Learning Composable Laws from Observed Trajectories"
Tuesday, September 8, 2020
5:00 p.m.-6:00 p.m.
For More Information:
Lena Johnson
301 405 8870
ljohns14@umd.edu

DPS Online Seminar

From Copernicus-Bachy-Kepler to Swarms: Learning Composable Laws from Observed Trajectories

5-6pm US ET (17-18, Paris-Madrid-Berlin Time)

Zoom link

Meeting ID: 940 2599 4687

Password: 407693

Dr. John Baras
Distinguished University Professor
Department of Electrical and Computer Engineering
Institute for Systems Research 
University of Maryland

Abstract
A novel approach is described, rooted in the port-Hamiltonian formalism on multi-layered graphs, for modeling, learning and analysis of governing laws of physical systems. The problem is inspired by the discovery by Kepler of the laws governing planetary motion from the data collected by Copernicus. We focus on learning the coordination laws of ensembles of autonomous multi-agent systems from empirical indexed trajectories data (position, velocity, etc.). Natural collectives like flocking birds, insects, and fish are included. We describe our results on validation of the applicability of universal port-Hamiltonian models (single and multi-agent) for an efficient learning framework to physics and biology related processes. We employ methods and techniques from mathematical physics for efficient and scalable learning (symmetries, invariants, conservation laws, Noether’s theorems, sparse learning, model reduction). We describe the modeling and software implementation of the methodology via deep learning platforms and efficient numerical schemes. We validate the performance on simulated ensemble data, generated by multiple potentials (various forms of Cucker-Smale models) and Boids models, with complex behaviors and maneuvers of autonomous swarms. We investigate the identification of leaders and sub-swarm motions. We apply mean-field theory to derive macroscopic (PDE) models of the ensemble that lead to explanations of certain coordination laws observed in bird flocks. Finally, we apply these methods to the control of several DPS physics-based systems described by PDEs.



   

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