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Physics-Informed Neural Networks
2021Physics-informed neural networks (PINNs) are used for problems where data are scarce. The underlying physics is enforced via the governing differential equation, including the residual in the cost function. PINNs can be used for both solving and discovering differential equations.
Stefan Kollmannsberger +3 more
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Self-adaptive physics-informed neural networks
Journal of Computational Physics, 2022Levi D. McClenny, Ulisses M. Braga-Neto
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Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
Journal of Scientific Computing, 2022Fabio Giampaolo +2 more
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hp-VPINNs: Variational physics-informed neural networks with domain decomposition
Computer Methods in Applied Mechanics and Engineering, 2021Zhongqiang Zhang +2 more
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Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Computer Methods in Applied Mechanics and Engineering, 2022Lu Lu, George Karniadakis, Xuhui Meng
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Physics informed neural networks for continuum micromechanics
Computer Methods in Applied Mechanics and Engineering, 2022Henning Wessels, Alexander Henkes
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Physics-informed neural networks for the shallow-water equations on the sphere
Journal of Computational Physics, 2022Alexander Bihlo, Roman Popovych
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Self-adaptive physics-informed neural networks
Journal of Computational Physics, 2023Ulisses Braga-Neto
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Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
Computer Methods in Applied Mechanics and Engineering, 2022Kevin Linka +2 more
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