Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next. (arXiv:2201.05624v2 [cs.LG] UPDATED)

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

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