Global Convergence of Deep Galerkin and PINNs Methods for Solving Partial Differential Equations

• URL: http://arxiv.org/abs/2305.06000v1
• Date: Wed, 10 May 2023 09:20:11 GMT
• Title: Global Convergence of Deep Galerkin and PINNs Methods for Solving Partial Differential Equations
• Authors: Deqing Jiang, Justin Sirignano, Samuel N. Cohen
• Abstract summary: A variety of deep learning methods have been developed to try and solve high-dimensional PDEs by approximating the solution using a neural network. We prove global convergence for one of the commonly-used deep learning algorithms for solving PDEs, the Deep Galerkin MethodDGM.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Numerically solving high-dimensional partial differential equations (PDEs) is a major challenge. Conventional methods, such as finite difference methods, are unable to solve high-dimensional PDEs due to the curse-of-dimensionality. A variety of deep learning methods have been recently developed to try and solve high-dimensional PDEs by approximating the solution using a neural network. In this paper, we prove global convergence for one of the commonly-used deep learning algorithms for solving PDEs, the Deep Galerkin Method (DGM). DGM trains a neural network approximator to solve the PDE using stochastic gradient descent. We prove that, as the number of hidden units in the single-layer network goes to infinity (i.e., in the ``wide network limit"), the trained neural network converges to the solution of an infinite-dimensional linear ordinary differential equation (ODE). The PDE residual of the limiting approximator converges to zero as the training time $\rightarrow \infty$. Under mild assumptions, this convergence also implies that the neural network approximator converges to the solution of the PDE. A closely related class of deep learning methods for PDEs is Physics Informed Neural Networks (PINNs). Using the same mathematical techniques, we can prove a similar global convergence result for the PINN neural network approximators. Both proofs require analyzing a kernel function in the limit ODE governing the evolution of the limit neural network approximator. A key technical challenge is that the kernel function, which is a composition of the PDE operator and the neural tangent kernel (NTK) operator, lacks a spectral gap, therefore requiring a careful analysis of its properties.

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