Surrogate Based

Surrogate-based optimization tackles the computational expense of evaluating complex models by replacing them with faster, approximate surrogates. Current research focuses on improving surrogate accuracy and efficiency using diverse machine learning models, including neural networks, random forests, Gaussian processes, and polynomial approximations, often within Bayesian optimization or evolutionary algorithm frameworks. This approach significantly accelerates optimization in various fields, from engineering design and materials science to climate modeling and marine ecosystem studies, enabling more efficient exploration of complex design spaces and parameter landscapes. The resulting speedups allow for more extensive simulations and improved design optimization.