Physics‐Driven Deep Neural Networks for Solving the Optimal Transport Problem Associated With the Monge–Ampère Equation

Monge–Ampère equations (MAEs) are fully nonlinear second‐order partial differential equations (PDEs), which are closely related to various fields including optimal transport (OT) theory, geometrical optics and affine geometry. Despite their significance, MAEs are extremely challenging to solve. Although some classical numerical approaches can solve MAEs, their computational efficiency deteriorates significantly on fine grids, with convergence often heavily dependent on the quality of initial estimate. Research on deep learning methods for solving MAEs is still in its early stages, which predominantly addresses simple formulations with basic Dirichlet boundary conditions. Here, we propose a deep learning method based on physics‐driven deep neural networks, enabling the solution of both simple and generalised MAEs with transport boundary conditions. In this method, we deal with two first‐order sub‐equations separated from MAE instead of solving the single MAE directly, which facilitates the imposition of transport boundary conditions and simplifies the training of neural networks. Moreover, we constrain the convexity of solution using the Lagrange multiplier method and maintain the optimisation process differentiable with bilinear interpolation. We provide three progressively complex examples ranging from a simple MAE with an analytical solution to a highly nonlinear variant arising in phase retrieval to validate the effectiveness of our method. For comparison, we benchmark against state‐of‐the‐art deep learning approaches that have been systematically adapted to accommodate the specific requirements of each example.