Mode conversion in non-homogeneous elastic media makes it challenging to interpret physical properties accurately. Decomposing these modes correctly is crucial across various scientific areas. Recent machine learning approaches have been proposed to address this problem, utilizing the Helmholtz decomposition technique. In this paper, we investigate the capabilities of a physics-informed neural network (PINN) in separating P and S modes by solving a scalar Poisson equation. This scalar formulation offers a dimensionally scalable reduction in computational cost compared to the traditional vector formulation. We verify the proposed method in both homogeneous and realistic non-homogeneous elastic models as showcases. The obtained separated modes closely match those from conventional numerical techniques, while exhibiting reduced transverse wave leakage.
@article{arxiv.2508.04600,
title = {Physics-Informed Neural Network for Elastic Wave-Mode Separation},
author = {E. A. B. Alves and P. D. S. de Lima and D. H. G. Duarte and M. S. Ferreira and J. M. de Araújo and C. G. Bezerra},
journal= {arXiv preprint arXiv:2508.04600},
year = {2026}
}