Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning (ML) and big data to improve the understanding of coupled, dynamic systems.

Physics-informed machine learning allows scientists to use this prior knowledge to help the training of the neural network, making it more efficient. This means it will need fewer samples to train it well or to make the training more accurate.

What is Scientific Machine Learning? Scientific Machine Learning brings together the complementary perspectives of computational science and computer science to craft a new generation of machine learning methods for complex applications across science and engineering.