SOURCE-LINKED INTELLIGENCE

Learning Interaction Kernels from Collective Steady States

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

We propose a learning procedure for system identification in interacting particle systems from single-snapshot observations of collective behaviors, unlike existing approaches that rely on observations of trajectories. This setting leads to a fundamentally ill-posed inverse problem, which we solve by using a regularization strategy based on the empirical distribution of observed configurations, drawn from different, unobserved initial conditions. We test our learning procedure on a variety of representative models with steady-state and quasi-stationary patterns, where collective behaviors enco

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