Learning Interaction Kernels from Collective Steady States
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 encode implicit information about the interaction mechanisms, demonstrating that our approach enables...
| Authors | Baoli Hao, Mauro Maggioni, Ming Zhong |
|---|---|
| Primary category | stat.ML |
| Published | 10 September 2026 |
| Last updated at source | 16 September 2026 |
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