Energy Regularization

Energy regularization is a technique used to improve the performance and robustness of various machine learning models by incorporating energy-based considerations into the learning process. Current research focuses on applications ranging from efficient sampling in complex systems (e.g., using Boltzmann samplers and diffusion models) to optimizing energy consumption in robotics and improving the accuracy of physical simulations (e.g., through physics-informed neural networks). This approach enhances model generalization, particularly in handling imbalanced data or outliers, leading to more reliable predictions and improved efficiency in diverse fields like materials science, robotics, and anomaly detection.