A broadband modeling method for range-independent underwater acoustic channels using physics-informed neural networks.

Accurate broadband modeling of underwater acoustic channels is vital for underwater acoustic detection, localization, and communication. Conventional modeling methodologies, based on methods such as the finite element method, finite difference method, and boundary element method, generally facilitate computation for only a single frequency at a time. However, in broadband modeling, this characteristic presents limitations, requiring multiple computations across frequencies, thereby leading to significant time challenges. To solve this problem, we propose a rapid broadband modeling approach using physics-informed neural networks. By integrating the modal equation of normal modes as a regularization term within the neural network's loss function, the method can achieve rapid broadband modeling of underwater acoustic channel with a sparse set of frequency sampling points. Operating in range-independent underwater environments with a liquid semi-infinite seabed, the method proficiently predicts the channel response across the frequency band from 100 to 300 Hz. Compared to the results obtained from KRAKEN, our method improves computational speed by a factor of 25 at a propagation distance of 20 km, while maintaining a mean absolute error of 0.15 dB for the acoustic channel response.