Web: http://arxiv.org/abs/2201.05624

Jan. 24, 2022, 2:11 a.m. | Salvatore Cuomo, Vincenzo Schiano di Cola, Fabio Giampaolo, Gianluigi Rozza, Maizar Raissi, Francesco Piccialli

cs.LG updates on arXiv.org arxiv.org

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, and integral-differential equations. This novel methodology has
arisen as a multi-task learning framework in which a NN must fit observed data
while reducing a PDE residual. This article provides a comprehensive review of
the literature on PINNs: while the primary goal of the study was to …

arxiv learning machine machine learning networks neural neural networks physics

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