Deep learning approach for designing acoustic absorbing metasurfaces with high degrees of freedom

The advent of the acoustic metasurfaces offered an unprecedented expansion of our ability to manipulate and structure sound waves for many exotic functionalities. However, the metasurface designs require deep expertise in acoustics and highly intensive iterative computations using the Finite Element Method. In this work, we use a two-dimensional convolutional neural network to model complex metasurface absorber structures under oblique wave incidences. The proposed network architecture is capable of computing the absorption spectra with a large number of design degrees of freedom. Further, we implement a conditional generative adversarial network to solve the inverse design problem. When fed with an input set of predefined absorption spectra, the constructed generative network produces candidate designs of metasurface absorbers that match on-demand spectra with high fidelity. We demonstrate the capability of the implemented network architecture by designing a metasurface absorber operating at 82 Hz for oblique wave incidences with a thickness of λ/64. To implement the deep learning methods for acoustic designs, the main challenge is to generate large and high-quality training dataset using numerical simulations. To mitigate this issue, we implement data augmentation. The presented approaches open new avenues to automate the design process of metasurfaces and enable a much more generalized and broader scope of optimal designs that go beyond acoustics applications.