Physical System

Research on physical systems increasingly leverages machine learning to model and predict their behavior, moving beyond traditional physics-based approaches. Current efforts focus on developing data-driven models, such as physics-informed neural networks (PINNs), graph neural networks (GNNs), and neural ordinary differential equations (NODEs), to accurately represent complex dynamics and incorporate physical constraints. This interdisciplinary approach promises to improve the efficiency and accuracy of simulations, enabling better control of complex systems and facilitating scientific discovery across diverse fields, from materials science to robotics.