Theoretical Physic

Theoretical physics is currently undergoing a transformation driven by the integration of machine learning techniques. Research focuses on developing and applying novel neural network architectures, such as physics-informed neural networks (PINNs) and variants of Kolmogorov-Arnold networks, to solve complex differential equations and model physical phenomena across diverse fields, from fluid dynamics and quantum physics to material science and robotics. This interdisciplinary approach promises to accelerate scientific discovery by enabling more efficient simulations, improved data analysis, and the potential discovery of new physical laws through the analysis of statistical patterns in existing equations.