Chun-Wei Kong

Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs

Stochastic differential equations are commonly used to describe the evolution of stochastic processes. The state uncertainty of such processes is best represented by the probability density function (PDF), whose evolution is governed by the Fokker-Planck partial differential equation (FP-PDE). Howev