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Recently, physics-informed neural networks (PINNs) have offered a powerful new paradigm for solving problems relating to differential equations.
The convergence rate of neural networks for learned functions of different frequencies
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Sifan Wang, Yujun Teng, and Paris Perdikaris · 2001
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Yeonjong Shin, J´erˆome Darbon, and George Em Karniadakis · 2004
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Physics-informed learning of governing equations from scarce data
Zhao Chen, Yang Liu, and Hao Sun · 2005
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Extreme learning machine: Theory and applications
Guang Bin Huang, Qin Yu Zhu, and Chee Kheong Siew · 2005
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Solving the wave equation with physics-informed deep learning
Ben Moseley, Andrew Markham, and Tarje Nissen-Meyer · 2006
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Siddhartha Mishra and Roberto Molinaro · 2006
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When and why Pinns Fail to Train: A Neural Tangent Kernel Perspective, jul 2020b
Sifan Wang, Xinling Yu, and Paris Perdikaris · 2007
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An unsplit convolutional perfectly matched layer improved at grazing incidence for the seismic wave equation
Dimitri Komatitsch and Roland Martin · 2007
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Theory of adaptive finite element methods: An introduction
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Physics-Informed Neural Networks for Cardiac Activation Mapping
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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
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A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges, jun 2015
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PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
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hp-VPINNs: Variational physics-informed neural networks with domain decomposition
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