Physical Law

Physical law learning aims to automate the discovery and representation of governing equations from data, bridging the gap between empirical observation and theoretical understanding. Current research focuses on developing machine learning models, including deep learning architectures and symbolic regression techniques, often incorporating physics-based constraints to improve accuracy and interpretability. These methods are being applied to diverse fields, from fluid dynamics and astrophysics to material science and medical imaging, promising to accelerate scientific discovery and enable more efficient modeling of complex systems. A key challenge lies in ensuring the uniqueness and reliability of the learned laws, requiring a robust theoretical framework alongside algorithmic advancements.