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Physics and equality constrained artificial neural networks: Application to forward and inverse problems with multi-fidelity data fusion

Basir, Shamsulhaq and Senocak, Inanc (2022) Physics and equality constrained artificial neural networks: Application to forward and inverse problems with multi-fidelity data fusion. JOURNAL OF COMPUTATIONAL PHYSICS, 463. ISSN 0021-9991

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Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Basir, Shamsulhaq
Senocak, InancSENOCAK@pitt.eduSENOCAK0000-0003-1967-7583
Date: 15 August 2022
Date Type: Publication
Journal or Publication Title: JOURNAL OF COMPUTATIONAL PHYSICS
Volume: 463
DOI or Unique Handle: 10.1016/j.jcp.2022.111301
Schools and Programs: Swanson School of Engineering > Mechanical Engineering and Materials Science
Refereed: No
Uncontrolled Keywords: Constrained optimization, Augmented Lagrangian method, Residual neural networks, Partial differential equations, Forward and Inverse problems, Multi-fidelity learning
ISSN: 0021-9991
Date Deposited: 31 May 2022 19:41
Last Modified: 17 Aug 2022 07:55
URI: http://d-scholarship.pitt.edu/id/eprint/43060

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