Physic Learner

Physics learners are a class of machine learning models designed to discover and represent the underlying physical laws governing observed phenomena from data, often expressed as symbolic equations. Current research focuses on developing novel architectures, such as physics-informed symbolic networks and those employing Monte Carlo tree search, to improve the accuracy, interpretability, and efficiency of these models in diverse applications, including modeling ground motion and fluid dynamics. These advancements offer the potential to enhance scientific understanding by automating the process of equation discovery and enabling more accurate predictions in various fields, ranging from earthquake engineering to materials science. The ability to learn and represent physical laws symbolically also promises improved model explainability and generalization compared to purely data-driven approaches.