Optimal Parameter

Determining optimal parameters is crucial across diverse scientific and engineering fields, aiming to maximize desired outcomes while minimizing costs or risks. Current research focuses on developing efficient algorithms, including Bayesian optimization, reinforcement learning, and metaheuristics like simulated annealing, often coupled with machine learning models such as neural networks and Gaussian Process Regression, to navigate complex, high-dimensional parameter spaces. These advancements enable efficient optimization in scenarios with limited data or computationally expensive evaluations, impacting fields ranging from materials science and manufacturing to quantum computing and machine learning model improvement. The resulting optimized parameters lead to improved performance, reduced costs, and enhanced reliability in various applications.