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While physics-informed neural networks (PINNs) have become a popular deep learning framework for tackling forward and inverse problems governed by partial differential equations (PDEs), their performance is known to degrade when larger and deeper neural network architectures are employed.
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Inverse dirichlet weighting enables reliable training of physics informed neural networks
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Thermodynamically consistent physics-informed neural networks for hyperbolic systems
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High-resolution image synthesis with latent diffusion models
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Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for pdes
Siddhartha Mishra and Roberto Molinaro · 2022
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Partial differential equations
Lawrence C Evans · 2022
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Random weight factorization improves the training of continuous neural representations
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A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
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About optimal loss function for training physics-informed neural networks under respecting causality
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Tsonn: Time-stepping-oriented neural network for solving partial differential equations
Wenbo Cao and Weiwei Zhang · 2023
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Efficient physics-informed neural networks using hash encoding
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