|
Abstract The typical microstructural topology optimization for acoustic-structure interaction system is achieved through a cyclic iteration of response analysis, sensitivity calculation, and design variable updates, suffering from expensive computation cost and low computational efficiency. To address these issues, a microstructural topology optimization method based on Long-Short Term Memory (LSTM) neural network is proposed. The core idea of this method is to treat the microstructural configurations in topology optimization process as a time series. The LSTM network, known for its powerful ability to process sequential information, is used to learn the patterns of configuration evolution. The data set is generated through microstructural topology optimization process based on the finite element-boundary element coupling analysis. Numerical examples show that the trained LSTM network can accurately predict the optimization process and reduce much computational cost compared to the conventional optimization method. In addition, the influence aspects of LSTM network structure is discussed.
|
|
Received: 27 April 2025
Published: 27 August 2025
|
|
|
|
2025, Vol. 46 
