Black Box Function

Black-box function optimization focuses on efficiently finding optimal inputs for functions whose internal workings are unknown, requiring iterative evaluations to guide the search. Current research emphasizes developing robust and sample-efficient algorithms, employing diverse models like Gaussian processes, neural networks (including physics-informed and interpretable architectures), diffusion models, and generative models within Bayesian optimization frameworks. These advancements are crucial for tackling computationally expensive problems across various fields, including engineering design, hyperparameter tuning, and scientific experimentation, where direct analytical optimization is infeasible. The ultimate goal is to minimize the number of expensive function evaluations needed to locate optimal or near-optimal solutions.