Title: Random Neural Network Expressivity for Non-Linear Partial Differential Equations
Primary area: learning theory
Keywords: random neural networks, partial differential equations, approximation error, expressivity

Abstract: Neural networks with randomly generated hidden weights (RaNNs) have been extensively studied, both as a standalone learning method and as an initialization for fully trainable deep learning methods. In this work, we study RaNN expressivity for learning solutions to non-linear partial differential equations (PDEs). 
To achieve this, we derive approximation error bounds for time-dependent Sobolev functions and obtain a dimension-free approximation rate $\frac{1}{2}$. 
Our results  imply that RaNNs are capable of efficiently approximating solutions to complex non-linear PDEs. When applied to Physics-Informed Neural Networks (PINNs), our bounds imply that with high probability, the physics-informed training error converges to $0$ with convergence rate free from the curse of dimensionality. Our theoretical analysis is supported by numerical simulations on a benchmark PDE. In these experiments, RaNN-based PINNs achieve a similar accuracy to classical PINNs, while requiring significantly less training time.