Surrogate Function

Surrogate functions are simplified representations of complex, computationally expensive functions, used to accelerate optimization and decision-making processes across diverse fields. Current research focuses on developing efficient surrogate models, including neural networks (e.g., neural tangent kernels, Fourier neural operators) and Bayesian methods (e.g., Gaussian processes), often incorporating techniques like transfer learning and majorization-minimization to improve accuracy and efficiency. These advancements are impacting various applications, from hyperparameter optimization and protein design to facility location and carbon sequestration monitoring, by enabling faster and more cost-effective solutions to challenging problems. The development of robust and accurate surrogate functions remains a key area of ongoing investigation.