Physics-informed neural network with pretraining optimization for ocean acoustic field prediction

2025/11/01 by Juncong Tang, Haiqiang Niu · 1 voice
Computer Science · Earth and Planetary Sciences · Physics and Astronomy · #Model Reduction and Neural Networks #Neural Networks and Reservoir Computing #Underwater Acoustics Research

paper · pdf · doi:10.1121/10.0039871

openalex publication_date 2025/11/01 · openalex created_date 2025/11/14 · openalex updated_date 2026/07/28

Abstract

To address the challenge of capturing high-frequency features in ocean acoustic field prediction using Physics-Informed Neural Networks (PINNs), this study introduces an enhanced pretraining optimization approach, termed PreT-OceanPINN, based on a recently proposed OceanPINN framework, a PINN-based method for predicting ocean acoustic pressure fields. By implementing a two-stage strategy-pretraining in a hypothetical environment followed by fine-tuning with real-world data-PreT-OceanPINN significantly improves both prediction accuracy for high-frequency components and training efficiency. In the pretraining stage, the model is trained using envelope signals derived from a simulated environment, allowing it to internalize the underlying physical principles of sound propagation. During the fine-tuning stage, a limited amount of measured data are used to adapt the model to complex real-world conditions. Compared to the standard OceanPINN approach, PreT-OceanPINN delivers more accurate high-frequency predictions without increasing real data dependency, thus demonstrating clear performance advantages. The effectiveness of the proposed method is validated through numerical simulations and experimental data from the SWellEx-96 field experiment.

Citations

• Gaussian beam tracing for computing ocean acoustic fields
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
• Physics-Informed Neural Networks for Heat Transfer Problems
• When and why PINNs fail to train: A neural tangent kernel perspective
• Physics-informed neural networks (PINNs) for fluid mechanics: a review
• Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions
• Inversion of seismoacoustic data using genetic algorithms and a posteriori probability distributions

Discussions

• SPECIAL ISSUE: GENERATIVE AND PHYSICS-INFORMED ARTIFICIAL INTELLIGENCE Physics-informed neural network with pretraining optimization for ocean acoustic field prediction https://doi.org/10.1121/10.003 [bsky, 0 points, 0 comments]