Critical Investigation of Failure Modes in Physics-informed Neural Networks
Shamsulhaq Basir,
Inanc Senocak
Abstract:Several recent works in scientific machine learning have revived interest in the application of neural networks to partial differential equations (PDEs). A popular approach is to aggregate the residual form of the governing PDE and its boundary conditions as soft penalties into a composite objective/loss function for training neural networks, which is commonly referred to as physics-informed neural networks (PINNs). In the present study, we visualize the loss landscapes and distributions of learned parameters … Show more
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