Learning stochastic multiscale models through normalizing flows
Many systems in physics, engineering, and biology exhibit multiscale stochastic dynamics, where low-dimensional slow variables evolve under the influence of high-dimensional fast processes. In practice, observations are often limited to a single trajectory of the slow component, while the fast dynamics remain unobserved, making statistical learning challenging. Approaches based on partial differential equations (PDE), such as Fokker-Planck formulations, aim to characterize the evolution of probability densities, typically requiring dense space-time data or grid-based solvers. In contrast, we adopt a trajectory-based perspective and develop a data-driven framework for learning effective st...
| Authors | Anan Saha, Arnab Ganguly |
|---|---|
| Primary category | stat.ML |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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