Welcome to the PML repository for physics-informed neural networks. We will use this repository to disseminate our research in this exciting topic. Links for some useful publications:

• Fleet Prognosis with Physics-informed Recurrent Neural Networks: This paper introduces a novel physics-informed neural network approach to prognosis by extending recurrent neural networks to cumulative damage models. We propose a new recurrent neural network cell designed to merge physics-informed and data-driven layers. With that, engineers and scientists have the chance to use physics-informed layers to model parts that are well understood (e.g., fatigue crack growth) and use data-driven layers to model parts that are poorly characterized (e.g., internal loads).