Data-efficient Bayesian-guided design selection from large candidate sets: Application to hyperelastic stochastic metamaterials

From a pool of admissible designs, we aim to identify a structure that achieves a target macroscopic stress response. For each candidate, the response is obtained from a high-fidelity oracle, such as expensive computational homogenization or experiments. We consider cases in which (i) the geometry c