Andrew Qing He

Andrew Qing He, Wei Cai, Sihong Shao

We extend the Weak Adversarial Neural Pushforward Method to the Wigner transport equation governing the phase-space dynamics of quantum systems. The central contribution is a structural observation: integrating the nonlocal pseudo-differential potential operator against plane-wave test functions produces a Dirac delta that exactly inverts the Fourier transform defining the Wigner potential kernel, reducing the operator to a pointwise finite difference of the potential at two shifted arguments. This holds in arbitrary dimension, requires no truncation of the Moyal series, and treats the potential as a black-box function oracle with no derivative information. To handle the negativity of the Wigner quasi-probability distribution, we introduce a signed pushforward architecture that decomposes the solution into two non-negative phase-space distributions mixed with a learnable weight. The resulting method inherits the mesh-free, Jacobian-free, and scalable properties of the original framework while extending it to the quantum setting.

Andrew Qing He, Wei Cai

We extend the Weak Adversarial Neural Pushforward Method (WANPM) to the McKean-Vlasov mean-field Fokker-Planck equation. For the quadratic interaction kernel, the mean-field nonlinearity reduces to a batch sample mean, requiring no secondary sampling. We focus on the stationary problem, identifying key training subtleties: gradient flow through the self-consistent mean estimate is essential for uniqueness, and adversarial test function frequencies must be initialized at a sufficiently large scale to avoid spurious minimizers. A numerical benchmark on the 1D linear McKean-Vlasov equation confirms accurate recovery of the exact Gaussian stationary distribution.

Andrew Qing He, Wei Cai

In this paper, we extend the Weak Adversarial Neural Pushforward Method to the Fokker--Planck equation on compact embedded Riemannian manifolds. The method represents the solution as a probability distribution via a neural pushforward map that is constrained to the manifold by a retraction layer, enforcing manifold membership and probability conservation by construction. Training is guided by a weak adversarial objective using ambient plane-wave test functions, whose intrinsic differential operators are derived in closed form from the geometry of the embedding, yielding a fully mesh-free and chart-free algorithm. Both steady-state and time-dependent formulations are developed, and numerical results on a double-well problem on the two-sphere demonstrate the capability of the method in capturing multimodal invariant distributions on curved spaces.

Andrew Qing He

Computing pairwise Wasserstein distances is a fundamental bottleneck in data analysis pipelines. Motivated by the classical Kuratowski embedding theorem, we propose two neural architectures for learning to approximate the Wasserstein-2 distance ($W_2$) from data. The first, DeepKENN, aggregates distances across all intermediate feature maps of a CNN using learnable positive weights. The second, ODE-KENN, replaces the discrete layer stack with a Neural ODE, embedding each input into the infinite-dimensional Banach space $C^1([0,1], \mathbb{R}^d)$ and providing implicit regularization via trajectory smoothness. Experiments on MNIST with exact precomputed $W_2$ distances show that ODE-KENN achieves a 28% lower test MSE than the single-layer baseline and 18% lower than DeepKENN under matched parameter counts, while exhibiting a smaller generalization gap. The resulting fast surrogate can replace the expensive $W_2$ oracle in downstream pairwise distance computations.

Andrew Qing He, Wei Cai

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 $(-Δ)^{α/2}$ 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 $(-Δ_x)^{α/2} f = |w|^αf$ 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.

Wei Cai, Andrew Qing He

We propose ARDO method for solving PDEs and PDE-related problems with deep learning techniques. This method uses a weak adversarial formulation but transfers the random difference operator onto the test function. The main advantage of this framework is that it is fully derivative-free with respect to the solution neural network. This framework is particularly suitable for Fokker-Planck type second-order elliptic and parabolic PDEs.