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Can physics-informed neural networks beat the finite element method? [PDF]
IMA J Appl MathGrossmann TG +3 more
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A Compact Memristor Model Based on Physics-Informed Neural Networks. [PDF]
Micromachines (Basel)Lee Y, Kim K, Lee J.
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Enhancing convergence speed with feature enforcing physics-informed neural networks using boundary conditions as prior knowledge. [PDF]
Sci RepJahani-Nasab M, Bijarchi MA.
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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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Outlier-resistant physics-informed neural network
Physical Review ERecent advances in machine learning have introduced physics-informed neural networks (PINN) as a valuable tool for addressing dynamics through governing equations and experimental observations. Outliers can be present in measurements and significantly affect the accuracy of the solutions provided by PINN.
D. H. G. Duarte +2 more
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Self-adaptive physics-informed neural networks
Journal of Computational Physics, 2022Levi McClenny, Ulisses Braga-Neto
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hp-VPINNs: Variational physics-informed neural networks with domain decomposition
Computer Methods in Applied Mechanics and Engineering, 2021Ehsan Kharazmi
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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
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