Design space exploration for resonant metamaterials using physics guided neural networks

Abstract

Data-driven models have been increasingly used in recent years. However, their application to explore engineering design spaces only recently attracted attention. These design spaces are generally complex, which suits the use of such data-driven models, like neural networks. In this paper, a neural network is built and trained to create a surrogate model which enables the design of a resonator of a resonant metamaterial. For increased training efficiency of the neural network, physical relations are embedded in it. The trained neural network is used in design optimisation of a resonant metamaterial and benchmarked with an optimisation using finite elements. The optimisation using neural networks is shown to be computationally cheaper and yields to a better resonator design than the optimisation with finite elements. Moreover, the data dependency of neural networks is also studied. Therefore, this work shows the potential of neural networks to explore the design space of engineering design, in which multiple design tunings are required from a same geometry.