Parameter Optimization

Parameter optimization aims to find the optimal settings for a system's adjustable parameters to maximize performance or achieve a desired outcome. Current research focuses on developing efficient and robust algorithms, including Bayesian optimization, variants of gradient descent (like Adam and BAdam for large models), and genetic algorithms, often applied within frameworks like hyperband. These advancements are crucial for diverse applications, from improving the accuracy and efficiency of machine learning models and robotic control to optimizing complex systems like lithium-ion batteries and metasurfaces, ultimately leading to more effective and efficient designs across various scientific and engineering disciplines.