Autoencoder artificial neural network for accelerated forward and inverse design of locally resonant acoustic metamaterials

Abstract

The noise issues brought about by the development of the aviation and other industries have put forward an urgent demand for the design of low-frequency noise reduction structures. An autoencoder artificial neural network (ANN) is established in this paper to achieve accelerated low-cost forward and on demand design of locally resonant metamaterials simultaneously. Inspired by the framework of the autoencoder network, the proposed ANN is composed of an in series connected inverse prediction neural network and a forward prediction neural network module to avoid program errors by multisolution problems. A theoretical model is first set up in the paper to calculate the sound transmission loss (STL) of a locally resonant metamaterial plate and then validated by finite element simulation. The autoencoder ANN is subsequently trained using the dataset constructed based on the theoretical model. The accuracy of the well-trained ANN is then evaluated by making a comparison with the theoretical calculation and originally expected STL curves. The advantages of the proposed ANN over the theoretical model and numerical simulation are analyzed, and the results indicate that the proposed autoencoder ANN takes 2 and 6 orders of magnitude less time to complete the forward design than theoretical and numerical methods. The proposed ANN also demonstrates its ability in inverse design, which is hardly achieved using theoretical and numerical methods. The proposed ANN provides a new design method for accelerated forward and inverse design of noise reduction structures.