Physic Informed Machine Learning

Physics-informed machine learning (PIML) integrates physical laws and principles into machine learning models to improve prediction accuracy, robustness, and interpretability, particularly when data is scarce or noisy. Current research focuses on applying PIML to various problems using diverse architectures, including neural networks (e.g., Physics-Informed Neural Networks, DeepONets), Gaussian processes, and state-space models, often tailored to specific applications like dynamical systems modeling and solving partial differential equations. This hybrid approach offers significant advantages over purely data-driven or purely physics-based methods, impacting fields ranging from engineering and materials science to environmental modeling and healthcare through improved model accuracy and reduced computational costs.