{"ID":2882646,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09529","arxiv_id":"2508.09529","title":"DeepWKB: Learning WKB Expansions of Invariant Distributions for Stochastic Systems","abstract":"This paper introduces a novel deep learning method, called DeepWKB, for estimating the invariant distribution of randomly perturbed systems via its Wentzel-Kramers-Brillouin (WKB) approximation $u_ε(x) = Q(ε)^{-1} Z_ε(x) \\exp\\{-V(x)/ε\\}$, where $V$ is known as the quasi-potential, $ε$ denotes the noise strength, and $Q(ε)$ is the normalization factor. By utilizing both Monte Carlo data and the partial differential equations satisfied by $V$ and $Z_ε$, the DeepWKB method computes $V$ and $Z_ε$ separately. This enables an approximation of the invariant distribution in the singular regime where $ε$ is sufficiently small, which remains a significant challenge for most existing methods. Moreover, the DeepWKB method is applicable to higher-dimensional stochastic systems whose deterministic counterparts admit non-trivial attractors. In particular, it provides a scalable and flexible alternative for computing the quasi-potential, which plays a key role in the analysis of rare events, metastability, and the stochastic stability of complex systems.","short_abstract":"This paper introduces a novel deep learning method, called DeepWKB, for estimating the invariant distribution of randomly perturbed systems via its Wentzel-Kramers-Brillouin (WKB) approximation $u_ε(x) = Q(ε)^{-1} Z_ε(x) \\exp\\{-V(x)/ε\\}$, where $V$ is known as the quasi-potential, $ε$ denotes the noise strength, and $Q...","url_abs":"https://arxiv.org/abs/2508.09529","url_pdf":"https://arxiv.org/pdf/2508.09529v1","authors":"[\"Yao Li\",\"Yicheng Liu\",\"Shirou Wang\"]","published":"2025-08-13T06:23:06Z","proceeding":"math.DS","tasks":"[\"math.DS\",\"cs.LG\"]","methods":"[]","has_code":false}