SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition

Yihang Gao, Ka Chun Cheung, Michael K. Ng

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

2 Citations (Scopus)

Abstract

Physics-informed neural networks (PINNs) have attracted significant attention for solving partial differential equations (PDEs) in recent years because they alleviate the curse of dimensionality that appears in traditional methods. However, the most disadvantage of PINNs is that one neural network corresponds to one PDE. In practice, we usually need to solve a class of PDEs, not just one. With the explosive growth of deep learning, many useful techniques in general deep learning tasks are also suitable for PINNs. Transfer learning methods may reduce the cost for PINNs in solving a class of PDEs. In this paper, we proposed a transfer learning method of PINNs via keeping singular vectors and optimizing singular values (namely SVD-PINNs). Numerical experiments on high dimensional PDEs (10-d linear parabolic equations and l0-d Allen-Cahn equations) show that SVD-PINNs work for solving a class of PDEs with different but close right-hand-side functions.

Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
EditorsHisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett
PublisherIEEE
Pages1443-1450
Number of pages8
Edition1st
ISBN (Electronic)9781665487689
ISBN (Print)9781665487696
DOIs
Publication statusPublished - 4 Dec 2022
Event2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 - Singapore, Singapore
Duration: 4 Dec 20227 Dec 2022

Publication series

NameProceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022

Conference

Conference2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022
Country/TerritorySingapore
CitySingapore
Period4/12/227/12/22

Scopus Subject Areas

  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)
  • Computational Mathematics
  • Control and Optimization
  • Transportation

User-Defined Keywords

  • Physics Informed Neural Networks
  • Singular Value Decomposition
  • Transfer Learning

Fingerprint

Dive into the research topics of 'SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition'. Together they form a unique fingerprint.

Cite this