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Physics-Informed Neural Networks and Extensions

Authors :
Raissi, Maziar
Perdikaris, Paris
Ahmadi, Nazanin
Karniadakis, George Em
Publication Year :
2024

Abstract

In this paper, we review the new method Physics-Informed Neural Networks (PINNs) that has become the main pillar in scientific machine learning, we present recent practical extensions, and provide a specific example in data-driven discovery of governing differential equations.<br />Comment: Frontiers of Science Awards 2024

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2408.16806
Document Type :
Working Paper