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[02175] Learning Interaction laws in particle- and agent-based systems

  • Session Time & Room : 5D (Aug.25, 15:30-17:10) @E705
  • Type : Contributed Talk
  • Abstract : We consider the following inference problem for a system of interacting particles or agents: given only observed trajectories of the agents in the system, can we learn what the laws of interactions are? We would like to do this without assuming any particular form for the interaction laws, i.e. they might be “any” function of pairwise distances, or other variables. We discuss when this problem is well-posed, construct estimators for the interaction kernels with provably good statistically and computational properties, and discuss extensions to second-order systems, more general interaction kernels, and stochastic systems. We measure empirically the performance of our techniques on various examples, including families of systems with parametric interaction kernels, and settings where the interaction kernels depend on unknown variables. We also conduct numerical experiments to study the emergent behavior of these systems. This is joint work with F. Lu, J. Feng, P. Martin, J.Miller, S. Tang and M. Zhong.
  • Classification : 70F17, 62M20, 34A55
  • Format : Talk at Waseda University
  • Author(s) :
    • Mauro Maggioni (Johns Hopkins University)