On acoustic fields of complex scatters based on physics-informed neural networks

Abstract

This paper proposes a modeling method for scattered acoustic fields under complex structures based on Physics-informed Neural Networks (PINNs), with particular attention to the acquisition of training sets and the embedding of physical governing equations. First, using acoustic simulation softwares to obtain the scattered acoustic field under various models, and select the scattered acoustic field data at several moments as the training sets. Then, according to the characteristics of the simulated model, the corresponding physical equations have been embedded in the loss function of the network. We tested the method by predicting the propagation of ultrasonic waves and the scattering of acoustic fields with various simple scatterers. Furthermore, we also use PINN to simulate the scattered acoustic field of the real complex damaged structure. The results show that the mean square error (MSE) between prediction and ground truth is in the order of 10^-4, which illustrate PINN can effectively simulate the propagation and reflection of ultrasonic waves, and can also simulate the scattered acoustic field of complex structures accurately. The meshless and accurate characteristics of PINN provide a reliable alternative for the theoretical prediction of complex and continuous scattered acoustic fields.

Keywords: Damage detection; Machine learning; Physics-informed neural network; Scattered acoustic field.