Scientific Machine Learning

Scientific machine learning (SciML) integrates machine learning with physical principles to model and predict complex phenomena, aiming to overcome limitations of data-driven approaches alone. Current research heavily focuses on physics-informed neural networks (PINNs), neural operators, and other architectures designed to incorporate governing equations and physical constraints into the learning process, often addressing challenges like uncertainty quantification and efficient training. SciML's impact is significant, enabling faster and more accurate simulations across diverse scientific domains, from seismology and fluid dynamics to materials science and climate modeling, ultimately accelerating scientific discovery and technological advancement.