Paris Perdikaris, University of of Pennsylvania, When and why physics-informed neural networks fail to train: A neural tangent kernel perspective
ZoomPhysics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial differential equations. However, despite their noticeable empirical success, little is known about how such constrained neural networks behave during their training via gradient descent. More importantly, even less is known…