[2603.02231] Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction

Computer Science > Machine Learning arXiv:2603.02231 (cs) [Submitted on 13 Feb 2026] Title:Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction Authors:Huiwen Zhang, Feng Ye, Chu Ma View a PDF of the paper titled Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction, by Huiwen Zhang and 2 other authors View PDF HTML (experimental) Abstract:Large-scale wave field reconstruction requires precise solutions but faces challenges with computational efficiency and accuracy. The physics-based numerical methods like Finite Element Method (FEM) provide high accuracy but struggle with large-scale or high-frequency problems due to prohibitive computational costs. Pure data-driven approaches excel in speed but often lack sufficient labeled data for complex scenarios. Physics-informed neural networks (PINNs) integrate physical principles into machine learning models, offering a promising solution by bridging these gaps. However, standard PINNs embed physical principles only in loss functions, leading to slow convergence, optimization instability, and spectral bias, limiting their ability for large-scale wave field reconstruction. This work introduces architecture physics embedded (PE)-PINN, which integrates additional physical guidance directly into the neural network architecture beyond Helmholtz equations and boundary conditions in loss functions. Specifically, a new en...