2025/06/17 by Tobias Grafke, Grafke, Tobias
Decision Sciences · Physics and Astronomy · Engineering · #Probabilistic and Robust Engineering Design #Model Reduction and Neural Networks #Fluid Dynamics and Turbulent Flows
paper · pdf · doi:10.48550/arxiv.2506.15743
We present an algorithm to sample stochastic differential equations conditioned on rather general constraints, including integral constraints, endpoint constraints, and stochastic integral constraints. The algorithm is a pathspace Metropolis-adjusted manifold sampling scheme, which samples stochastic paths on the submanifold of realizations that adhere to the conditioning constraint. We demonstrate the effectiveness of the algorithm by sampling a dynamical condensation phase transition, conditioning a random walk on a fixed Levy stochastic area, conditioning a stochastic nonlinear wave equation on high amplitude waves, and sampling a stochastic partial differential equation model of turbulent pipe flow conditioned on relaminarization events.