Neural networks for inverse design of phononic crystals

Intelligent design of one-dimensional (1D) phononic crystals (PCs) by neural networks (NNs) is proposed. Two neural network models, supervised neural network (S-NN) and unsupervised neural network (U-NN), are used to realize the inverse design of PCs, concerning both geometric and physical parameter designs. Performances of the two models are compared and discussed. The results show that the bandgaps of the designed PCs by the two NNs are highly consistent with the target bandgaps. For the design of single or two parameters, the performances of the two NNs are excellent; while for the case of three-parameter design, U-NN works much better than S-NN due to the impact of non-uniqueness on S-NN. The present work confirms the feasibility of inverse design of PCs by NNs, and provides a useful reference for the application of NNs in the intelligent inverse design of 2D or 3D PCs.