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Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs).
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arXiv preprint arXiv:1912.12355
Softadapt: Techniques for adaptive loss weighting of neural networks with multi-part loss functions · 2019
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Solving allen-cahn and cahn-hilliard equations using the adaptive physics informed neural networks
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When and why pinns fail to train: A neural tangent kernel perspective
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Understanding and mitigating gradient pathologies in physics-informed neural networks
Sifan Wang, Yujun Teng, and Paris Perdikaris · 2020
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On the convergence and generalization of physics informed neural networks
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