Multi-scale physics-informed machine learning using the Buckingham pi theorem

DOI10.1016/J.JCP.2022.111810OpenAlexW4310856212WikidataQ121773573 ScholiaQ121773573MaRDI QIDQ2112525

David B. Doman, Justin D. Merrick, Michael W. Oppenheimer

Publication date: 11 January 2023

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2022.111810

zbMATH Keywords

neural networkmachine learningdimensional analysisphysics-informedBuckingham pi

Mathematics Subject Classification ID

Computer science (68-XX) Systems theory; control (93-XX)

Cites Work

• Scientific machine learning through physics-informed neural networks: where we are and what's next

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