Deep learning-assisted multifunctional wavefront modulation with Willis coupling

Abstract

Diverse wavefront modulations with multifunctional acoustic devices have been of great interest to physics and engineering communities. However, traditional design methods of multifunctional acoustic devices rely on a deterministic physical model and redundant iterative optimization, resulting in inflexibility and consuming of time. In this work, we present and experimentally implement a deep learning-assisted tunable acoustic metagrating for multifunctional wavefront modulation with 95.2% accuracy and a 105 order of magnitude decrease in computational time compared to a classical optimization method. The presented tunable structure formed by a periodic array of 3C-shaped unit cells excites controllable Willis coupling, exhibiting corresponding asymmetrical scattering patterns. With the support of a deep learning strategy, the optimal configuration between structure parameters and Willis coupling magnitude could be efficiently confirmed, realizing various extraordinary wavefront modulations, including abnormal reflection, perfect beam splitting, and multi-channel energy distribution in arbitrary ratios. The polarizability tensor retrieval method is used to characterize the Willis coupling of different modulation structures, demonstrating the refined abstraction of the deep learning strategy on Willis coupling. Meanwhile, the numerical and experimental results are in good agreement with the desired wavefront modulation, verifying the effectiveness of the proposed method. Our work develops deep learning-assisted multifunctional wavefront modulation with the advantages of high accuracy, efficiency, flexibility, and refined abstraction of a physical mechanism, paving the way for a combination of deep learning and pragmatic multifunctional acoustic applications.