Gradient Surgery for Physics-Informed Neural NetworksNew

This research focuses on Physics-Informed Neural Networks trained with composite objectives combining data fitting and physics-based constraints. The work analyzes conflicting task gradients during training and addresses their impact on slow convergence and unstable optimization, particularly for stiff and high-frequency partial differential equations.