Comparison of traditional and deep learning optimisation for the design of acoustic metamaterials

This paper presents a comparative analysis of traditional and Deep learning (DL) optimisation approaches for AMM design. A case study comparing a generalised pattern search (GPS) optimisation to a previously trained inverse design deep neural network (IDDNN) of an acoustic meta-absorber using various metrics is considered. For benchmarking, a baseline design is realised with arbitrarily chosen geometric parameters in a CAD software. This design is 3D printed and experimentally tested. The peak absorption and corresponding resonant frequency obtained from experimental absorption, along with other geometric parameters, are used as inputs to IDDNN and GPS. Results show that IDDNN provides superior inference speed, requiring 7.24e-3 [s] to generate predictions on a single input instance, compared to GPS which took about 30.5464 [s]. While GPS exhibits slightly closer alignment to experimental curve based on Kullback-Leibler divergence (2.79e-3) compared to IDDNN (3.31e-3), IDDNN still achieves a slightly higher accuracy of 92%, compared to 90% for GPS. It is emphasised that trade-offs may be necessary in practice to determine the most suitable design approach for a given application. Traditional optimisation offers ease of design flexibility, allowing for on-the-fly model improvement and modification. In contrast, DL may require re-training, leading to delays in process workflow