Design Optimization

Design optimization aims to find the best design parameters for a system, minimizing costs and maximizing performance, often under complex constraints. Current research focuses on applying advanced algorithms like deep reinforcement learning, evolutionary metaheuristics, and Bayesian optimization, often coupled with surrogate models (e.g., Gaussian processes, neural operators) to handle computationally expensive simulations. These techniques are being used across diverse fields, from robotics and engineering design to material science and nuclear fusion, improving efficiency and enabling the exploration of novel designs. The resulting optimized designs lead to significant improvements in performance, cost reduction, and accelerated development cycles in various applications.