SOURCE-LINKED INTELLIGENCE

Deep operator learning for efficient sampling from invariant measures of stochastic differential equations

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

We introduce an amortized neural sampler that combines operator learning with flow methods for sampling. It maps SDE coefficient functions to pushforwards from a reference measure to the invariant measures, enabling efficient sampling across families of stochastic differential equations. Our framework shifts traditional sampling cost to an initial training phase, after which new SDE instances require only one encoder pass and a few ODE solver steps, independent of mixing time. To handle problems in high dimensions, we use Lagrangian trajectory sensors for the coefficient functions and cross at

Read original source ↗ Open in workspace

recordType

paper

region

Global