Stochastic Physics-Constrained Operator Inference for Complex Systems

The paper introduces stochastic physics-constrained operator inference for quadratically nonlinear systems driven by additive noise. SPOpInf learns a stochastic differential equation, including quadratic-linear drift and diffusion amplitude, directly from data without intrusive model modifications. The approach targets stochastic dynamical systems and reduced-order models of turbulent flows.