Discovery of interaction and diffusion kernels in particle-to-mean-field multi-agent systems
We propose a data-driven framework to learn interaction kernels in stochastic multi-agent systems. Our approach aims at identifying the functional form of nonlocal interaction and diffusion terms directly from trajectory data, without any a priori knowledge of the underlying interaction structure. Starting from a discrete stochastic binary-interaction model, we formulate the inverse problem as a sequence of sparse regression tasks in structured finite-dimensional spaces spanned by compactly supported basis functions, such as piecewise linear polynomials. In particular, we assume that pairwise interactions between agents are not directly observed and that only limited trajectory data are a...
| Authors | Giacomo Albi, Alessandro Alla, Elisa Calzola |
|---|---|
| Primary category | cs.LG |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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