AI RESEARCH
Adaptive Probability Flow Residual Minimization for High-Dimensional Fokker-Planck Equations
arXiv CS.LG
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ArXi:2512.19196v3 Announce Type: replace-cross Solving high-dimensional Fokker-Planck (FP) equations is a challenge in computational physics and stochastic dynamics, due to the curse of dimensionality (CoD) and unbounded domains. Existing deep learning approaches, such as Physics-Informed Neural Networks, face computational challenges as dimensionality increases, driven by the $O(d^2)$ complexity of automatic differentiation for second-order derivatives.