Learning the solution operator of parametric partial differential equations with physics-informed DeepONets.

ABSTRACT: Partial differential equations (PDEs) play a central role in the mathematical analysis and modeling of complex dynamic processes across all corners of science and engineering. Their solution often requires laborious analytical or computational tools, associated with a cost that is markedly amplified when different scenarios need to be investigated, for example, corresponding to different initial or boundary conditions, different inputs, etc. In this work, we introduce physics-informed DeepONets, a deep learning framework for learning the solution operator of arbitrary PDEs, even in the absence of any paired input-output training data. We illustrate the effectiveness of the proposed framework in rapidly predicting the solution of various types of parametric PDEs up to three orders of magnit

SUBMITTER: Wang S 

PROVIDER: S-EPMC8480920 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature