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

Journal of Scientific Computing, 2022
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.
Fabio Giampaolo, Vincenzo Schiano di Cola, Gianluigi Rozza   +2 more
exaly   +2 more sources

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-VPINNs: Variational physics-informed neural networks with domain decomposition

Computer Methods in Applied Mechanics and Engineering, 2021
Zhongqiang Zhang, Ehsan Kharazmi, George Karniadakis   +2 more
exaly  

A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures

Engineering Fracture Mechanics, 2021
Jian-Guo Gong, Fu-Zhen Xuan
exaly  

Self-adaptive physics-informed neural networks

Journal of Computational Physics, 2023
Braga-Neto, Ulisses
exaly  

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

Computer Methods in Applied Mechanics and Engineering, 2022
Lu Lu, George Karniadakis, Xuhui Meng
exaly  

Physics-informed neural networks for the shallow-water equations on the sphere

Journal of Computational Physics, 2022
Alexander Bihlo, Roman O Popovych
exaly  

Physics informed neural networks for continuum micromechanics

Computer Methods in Applied Mechanics and Engineering, 2022
Henning Wessels, Rolf D. Mahnken, Alexander Henkes   +2 more
exaly  

A physics-informed neural network approach to fatigue life prediction using small quantity of samples

International Journal of Fatigue, 2023
Yazhi Li
exaly  

Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

Journal of Computational Physics, 2020
Ameya D Jagtap, George Karniadakis, Kenji Kawaguchi   +2 more
exaly