Wave equation-informed generative networks for the design of speech recognition acoustic materials

The inverse problem of designing materials to achieve desired acoustic functionality while strictly adhering to acoustic principles remains an unresolved scientific challenge. This work introduces a generative adversarial network based on the wave equation (Wave-GAN), in which the governing equation is directly embedded into the training process to iteratively optimize the material density distribution. The physics-guided framework enables the model to learn acoustic patterns directly from sound and generate material distributions with specified functionalities. As a verification example, a speaker recognition task was conducted. The results demonstrate that Wave-GAN produces physically consistent materials, achieving a recognition accuracy of 95.6%. This opens a promising direction for fully physics-driven material design at the interface of acoustics and machine learning.