Rapid design of metamaterials via multitarget Bayesian optimization

Composed of a large number of subwavelength unit cells with designable geometries, metamaterials have been widely studied to achieve extraordinary advantageous and unusual optical properties. However, ordinary computer simulator requires a time-consuming fine-tuning to find a proper design of metamaterial for a specific optical property, making the design stage a critical bottleneck in large scale applications of metamaterials. This paper investigates the metamaterial design under the framework of computer experiments, with emphasis on dealing with the challenge of designing numerous unit cells with functional responses, simultaneously, which is not common in traditional computer experiments. We formulate the multiple related design targets as a multitarget design problem. Leveraging on the similarity between different designs, we propose an efficient Bayesian optimization strategy with a parsimonious surrogate model and an integrated acquisition function to design multiple unit cells with very few function evaluations. A wide range of simulations confirm the effectiveness and superiority of the proposed approach compared to the naive strategies where the multiple unit cells are dealt with separately or sequentially. Such a rapid design strategy has the potential to greatly promote large scale applications of metamaterials in practice.