标题： 采样条件扩散过程的路径空间投影蒙特卡洛方法 显示英文标题

标题： Sampling conditioned diffusions via Pathspace Projected Monte Carlo

摘要： 我们提出了一种算法，用于对受相当一般约束条件（包括积分约束、端点约束和随机积分约束）的随机微分方程进行采样。 该算法是一种路径空间修正的流形采样方案，能够在满足约束条件的子流形上对随机路径进行采样。 我们通过以下实例展示了该算法的有效性：对动力学凝聚相变进行采样，对固定莱维随机面积下的随机游走进行条件采样，在高振幅波条件下对随机非线性波动方程进行采样，并对受层流事件约束的湍流管道流动的随机偏微分方程模型进行采样。 显示英文摘要

摘要： 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.