Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. PINNs are nowadays used to solve PDEs, fractional equations, integral-differential equations, and stochastic PDEs. Th...

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Veröffentlicht in:	Journal of scientific computing Jg. 92; H. 3; S. 88
Hauptverfasser:	Cuomo, Salvatore, Di Cola, Vincenzo Schiano, Giampaolo, Fabio, Rozza, Gianluigi, Raissi, Maziar, Piccialli, Francesco
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.09.2022 Springer Nature B.V
Schlagworte:	Algorithms Applied mathematics Approximation Artificial intelligence Beyond traditional AI: the impact of Machine Learning on Scientific Computing Boundary conditions Computational Mathematics and Numerical Analysis Deep learning Finite element method Literature reviews Machine learning Mathematical analysis Mathematical and Computational Engineering Mathematical and Computational Physics Mathematics Mathematics and Statistics Neural networks Partial differential equations Physics Taxonomy Theoretical Nonlinear equations Uncertainty Physics–Informed Neural Networks Numerical methods Deep Neural Networks Scientific Machine Learning Partial Differential Equations
ISSN:	0885-7474, 1573-7691
Online-Zugang:	Volltext
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