Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?

arXiv:2405.04230v1 Announce Type: cross
Abstract: This study investigates the potential accuracy boundaries of physics-informed neural networks, contrasting their approach with previous similar works and traditional numerical methods. We find that selecting improved optimization algorithms significantly enhances the accuracy of the results. Simple modifications to the loss function may also improve precision, offering an additional avenue for enhancement. Despite optimization algorithms having a greater impact on convergence than adjustments to the loss function, practical considerations often favor tweaking the latter due …

abstract accuracy algorithms arxiv cs.ai cs.lg networks neural networks numerical optimization physics physics.comp-ph physics-informed process results simple study type