A Physics-Constrained Learning Framework for Wave Propagation in Complex Poroelastic Multilayered Media

TL;DR

This paper introduces PCL-CMM, a physics-constrained learning framework that combines poroelastic theory and neural networks to improve wave modelling and imaging in complex multilayered media.

Contribution

It develops a general framework integrating physical laws with deep learning to enhance wave propagation modelling and image reconstruction in heterogeneous media.

Findings

PCL-CMM effectively compensates for skull-induced acoustic distortions.

It improves SSIM by more than 0.06 over purely data-driven methods.

Demonstrated success in simulations and ex vivo experiments.

Abstract

Wave propagation through complex poroelastic multilayered media is difficult to model and invert because pronounced heterogeneity, scattering, mode conversion and fluid-solid coupling jointly distort acoustic signals during propagation. Here we present Physics-Constrained Learning for Complex Multilayered Media (PCL-CMM), a general framework that integrates Biot's poroelastic theory with the elastic wave equation to bridge the gap between physically rigorous wave modelling and data-driven learning. PCL-CMM constructs a high-fidelity digital twin that dynamically computes an effective acoustic stiffness tensor for forward wave modelling and incorporates the resulting physical constraint as a loss term to regularize the training of deep neural networks. We demonstrate PCL-CMM on transcranial photoacoustic imaging, where skull-induced acoustic distortions severely degrade image formation. Across simulations and ex vivo experiments, PCL-CMM effectively compensates for these distortions and improves SSIM by more than 0.06 compared with purely data-driven neural networks. This work establishes a physics-constrained learning framework for acoustic wave modelling in complex poroelastic multilayered media.