Divergence-Kernel method for scores of random systems

Fuente: arXiv
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Auteur principal: Ni, Angxiu
Format: Preprint
Publié: 2025
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author Ni, Angxiu
author_facet Ni, Angxiu
contents We derive the divergence-kernel formula for the scores of random dynamical systems, then formally pass to the continuous-time limit of SDEs. Our formula works for multiplicative noise systems over any period of time; it does not require hyperbolicity. We also consider several special cases: (1) for additive noise, we give a pure kernel formula; (2) for short-time, we give a pure divergence formula; (3) we give a formula which does not involve scores of the initial distribution. Based on the new formula, we derive a pathwise Monte-Carlo algorithm for scores, and demonstrate it on the 40-dimensional Lorenz 96 system with multiplicative noise.
format Preprint
id arxiv_https___arxiv_org_abs_2507_04035
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Divergence-Kernel method for scores of random systems
Ni, Angxiu
Probability
Numerical Analysis
Dynamical Systems
Optimization and Control
60H07, 60J60, 65D25, 65C30, 65C05
We derive the divergence-kernel formula for the scores of random dynamical systems, then formally pass to the continuous-time limit of SDEs. Our formula works for multiplicative noise systems over any period of time; it does not require hyperbolicity. We also consider several special cases: (1) for additive noise, we give a pure kernel formula; (2) for short-time, we give a pure divergence formula; (3) we give a formula which does not involve scores of the initial distribution. Based on the new formula, we derive a pathwise Monte-Carlo algorithm for scores, and demonstrate it on the 40-dimensional Lorenz 96 system with multiplicative noise.
title Divergence-Kernel method for scores of random systems
topic Probability
Numerical Analysis
Dynamical Systems
Optimization and Control
60H07, 60J60, 65D25, 65C30, 65C05
url https://arxiv.org/abs/2507.04035