This repository aggregates representative research papers on Physics-Informed Neural Networks (PINNs), a deep learning approach that embeds physical laws into neural network training to solve differential equations. It also references software libraries such as DeepXDE, SciANN, and NVIDIA SimNet that implement PINN methodologies for scientific computing applications including computational physics and uncertainty quantification.