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Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial differential equations (PDEs), and have been widely used in a variety of PDE problems.
The finite element method , volume 3
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Generative adversarial nets
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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Solving inverse stochastic problems from discrete particle observations using the fokker–planck equation and physics-informed neural networks
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Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for pdes
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B-pinns: Bayesian physics-informed neural networks for forward and inverse pde problems with noisy data
Yang, L., Meng, X., and Karniadakis, G. E · 2021
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