Abstract:The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The L 2 Physics-Informed Loss is the de-facto standard in training Physics-Informed Neural Networks. In this paper, we challenge this common practice by investigating the relationship between the loss function and the approximation quality of the learned solution. In particular, we leverage the concept of stability in the literature of partial differential equation to stud… Show more
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