A physics-informed generative adversarial network for advancing solutions in ocean acoustics

Advancements in artificial intelligence, notably the groundbreaking efforts in deep learning exemplified by physics-informed neural networks, have opened up innovative pathways for addressing intricate ocean acoustic problems. However, conventional physics-informed neural networks are limited in solving high-frequency forward and inverse problems. This paper introduces a novel physics-informed generative adversarial network integrating a forward-solving network (generator) and an inverse parameter-estimating network (discriminator). The generator network incorporates convolutional neural networks with hard-constrained boundary conditions and optimized loss functions to effectively predict the solution governed by the time-domain wave equation. For inverse problems, a discriminator is introduced for parameter estimation to complete the generative adversarial network. Furthermore, customized optimization strategies and an adaptive weighting loss function are devised to boost the training performance further. The test results of both forward and reverse cases show the advantage of our model over existing methods in terms of accuracy. The result indicates its vast potential for applications in ocean acoustics engineering.