Inverse design of acoustic metamaterial-based vibration control for offshore wind turbines

This paper presents an inverse design approach for mechanical metamaterial turbines utilising a deep learning algorithm. We first establish a theoretical model for metamaterial turbines using the spectral element method to analyse their vibration characteristics. Subsequently, we predict the vibration transmission properties of these turbines and achieve on-demand inverse design of metastructures through a fully connected deep-learning neural network model. The efficacy of the forward prediction and reverse design network models is validated using an inverse neural network. Our results demonstrate a strong agreement between the predicted and target values, affirming the feasibility of employing deep learning for on-demand meta-turbine design. This intelligent design approach holds promise for expediting and enhancing the design of metamaterials tailored for low-frequency vibration isolation in engineering structures.