Design of enlarged phononic bandgap 2.5D acoustic resonator via active learning and non-gradient optimization

Identifying the phononic crystal (PnC) with bandgap is a problematic process because all phononic crystals don’t have bandgap. Predicting the Phononic bandgaps (PnBGs) is a computationally expensive task. Here we explore the potential of machine learning (ML) tools to expedite the prediction and maximize the resonator based PnBG. The Gaussian process regression (GPR) model is trained to learn the relationship between complicated shape and band structure of cavity. Bayesian optimization (BO) derives a new shape by leveraging the fast inference of the trained model, which is updated with the augmentation of newly explored structures to escalate the prediction power over performance expansion through active learning. Artificial intelligence (AI) assisted optimization requires a small number of generations to achieve convergence. The obtained results are validated via experimental measurements.