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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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Hp-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  

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  

Self-Adaptive Physics-Informed Neural Networks

Journal of Computational Physics, 2023
Braga-Neto, Ulisses
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  

Self-Adaptive Loss Balanced Physics-Informed Neural Networks

Neurocomputing, 2022
Wen Yao, Zixue Xiang
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