Learning interaction kernels in heterogeneous systems of agents from multiple trajectories

Abstract

Systems of interacting particles or agents have wide applications in many disciplines such as Physics, Chemistry, Biology and Economics. These systems are governed by interaction laws, which are often unknown: estimating them from observation data is a fundamental task that can provide meaningful insights and accurate predictions of the behaviour of the agents. In this paper, we consider the inverse problem of learning interaction laws given data from multiple trajectories, in a nonparametric fashion, when the interaction kernels depend on pairwise distances. We establish a condition for learnability of interaction kernels, and construct estimators that are guaranteed to converge in a suitable $L^2$ space, at the optimal min-max rate for 1-dimensional nonparametric regression. We propose an efficient learning algorithm based on least squares, which can be implemented in parallel for multiple trajectories and is therefore well-suited for the high dimensional, big data regime. Numerical simulations on a variety examples, including opinion dynamics, predator-swarm dynamics and heterogeneous particle dynamics, suggest that the learnability condition is satisfied in models used in practice, and the rate of convergence of our estimator is consistent with the theory. These simulations also suggest that our estimators are robust to noise in the observations, and produce accurate predictions of dynamics in relative large time intervals, even when they are learned from data collected in short time intervals.

63 pages, revised various places

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• Learning Stochastic Dynamics with Statistics-Informed Neural Network
• Learning Anisotropic Interaction Rules from Individual Trajectories in a Heterogeneous Cellular Population
• Random Feature Models for Learning Interacting Dynamical Systems
• Learning interaction kernels in mean-field equations of 1st-order systems of interacting particles
• Learning interaction kernels in stochastic systems of interacting particles from multiple trajectories
• Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation
• Maximum likelihood estimation of potential energy in interacting particle systems from single-trajectory data
• Learning particle swarming models from data with Gaussian processes
• On the identifiability of interaction functions in systems of interacting particles
• Classification-Based Opinion Formation Model Embedding Agents' Psychological Traits
• Identifiability of interaction kernels in mean-field equations of interacting particles
• Learning Interaction Kernels for Agent Systems on Riemannian Manifolds
• Cluster Prediction for Opinion Dynamics from Partial Observations
• On the coercivity condition in the learning of interacting particle systems
• On Lyapunov Conditions for the Well-Posedness of McKean-Vlasov Stochastic Differential Delay Equations