Weak Adversarial Neural Pushforward Method for Fractional Fokker-Planck Equations
This addresses a computational challenge in modeling anomalous diffusion processes for physics and applied mathematics, but it is incremental as it adapts an existing method to a new equation type.
The paper tackled solving fractional Fokker-Planck equations by extending the Weak Adversarial Neural Pushforward Method to handle fractional Laplacians, achieving close agreement with benchmark particle simulations for a 1D case with α=1.5 over time t∈[0,2].
We extend the Weak Adversarial Neural Pushforward Method (WANPM) to fractional Fokker-Planck equations (fFPE), in which the classical Laplacian diffusion operator is replaced by the fractional Laplacian $(-Î)^{α/2}$ for $α\in (0, 2]$. The solution distribution is represented not as an explicit probability density function but as the pushforward of a simple base distribution through a time-parameterized neural network $F_\vartheta(t, x_0, r)$, which enforces the initial condition exactly by construction. The weak formulation of the fFPE is discretized via Monte Carlo sampling entirely without temporal discretization, and the resulting min-max objective is optimized adversarially against a set of plane-wave test functions. A key computational advantage is that plane waves are eigenfunctions of the fractional Laplacian, so $(-Î_x)^{α/2} f = |w|^αf$ is computed exactly and at no additional cost for any $α$. We validate the method on a one-dimensional fractional Fokker-Planck equation with a quadratic confining potential and $α= 1.5$, using a particle simulation based on symmetric $α$-stable Levy increments as a benchmark. The learned solution faithfully reproduces the transient probability distribution over $t \in [0, 2]$, and robust statistics confirm close agreement with the particle simulation, while standard deviation comparisons highlight why second-moment metrics are inappropriate for heavy-tailed ($α< 2$) distributions.