Conditional Score-Based Modeling of Effective Langevin Dynamics
Stochastic reduced-order models are widely used to represent the effective dynamics of complex systems, but estimating their drift and diffusion coefficients from data remains challenging. Standard approaches often rely on short-time trajectory increments, state-space partitioning, or repeated simulation of candidate models, which become unreliable or computationally expensive for high-dimensional systems, coarse temporal sampling, or unevenly sampled data. We introduce a data-driven calibration method based on a novel relationship between the coefficients of a stochastic reduced model and the conditional score of the finite-time transition density, defined as the gradient of the logarith...
| Authors | Ludovico T. Giorgini |
|---|---|
| Primary category | stat.ML |
| Published | 27 April 2026 |
| Last updated at source | 15 June 2026 |
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