Year: 2019
Type: preprint
Abstract: We proposed a framework for solving inverse problems in differential equations based on neural networks and automatic differentiation. Neural networks are used to approximate hidden fields. We analyze the source of errors in the framework and derive an error estimate for a model diffusion equation problem. Besides, we propose a way for sensitivity analysis, utilizing the automatic differentiation mechanism embedded in the framework. It frees people from the tedious and error-prone process of deriving the gradients. Numerical examples exhibit consistency with the convergence analysis and error saturation is noteworthily predicted. We also demonstrate the unique benefits neural networks offer at the same time: universal approximation ability, regularizing the solution, bypassing the curse of dimensionality and leveraging efficient computing frameworks.
Source: arXiv (Cornell University)
Authors: Xu, Kailai, Darve, Eric
Institution: Stanford University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2912465314