Energy Minimization

Energy minimization is a core computational problem across diverse scientific fields, aiming to find the lowest-energy state of a system, often represented by a mathematical function. Current research focuses on applying energy minimization techniques within various machine learning models, including diffusion models, recurrent neural networks, and transformers, often coupled with optimization algorithms like simulated annealing and deep equilibrium methods. These advancements are improving the efficiency and accuracy of tasks ranging from drug discovery and image generation to robotics and sustainable energy management, impacting both theoretical understanding and practical applications. The development of novel energy functions and optimization strategies continues to be a central theme, driving progress in diverse areas.