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Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
[article]
2022
arXiv
pre-print
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. 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
arXiv:2201.05624v4
fatcat:rezb3ctw3bamtfrswcwxlc2cvy