Physical Law Learning

Physical law learning uses machine learning to automatically discover governing equations from data, aiming to accelerate scientific discovery and improve model accuracy. Current research focuses on developing robust and reliable algorithms, including generative models, convolutional neural networks, and Bayesian hierarchical models, that can handle noisy data and account for model uncertainties, particularly in complex systems like many-body interactions and geophysical phenomena. This field is significant because it promises to uncover hidden physical relationships in diverse domains, from earthquake prediction to material science, by supplementing or replacing computationally expensive traditional methods. Furthermore, efforts are underway to ensure the uniqueness and interpretability of learned laws, enhancing the reliability and trustworthiness of these AI-driven scientific tools.