Applications of physics informed neural operators

Abstract

We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the https://github.com/shawnrosofsky/PINO_Applications/tree/main , an interactive https://shawnrosofsky.github.io/PINO_Applications/ to visualize the predictions of our PINOs, and a tutorial for their use at the https://www.dlhub.org .

Keywords

• physics informed deep learning
• surrogate model
• neural operators