Fourier Neural Operator Networks: A Fast and General Solver for the Photoacoustic Wave Equation

Understand

Simulation tools for photoacoustic wave propagation have played a key role in advancing photoacoustic imaging by providing quantitative and qualitative insights into parameters affecting image quality.

• Classical methods for numerically solving the photoacoustic wave equation relies on a fine discretization of space and can become computationally expensive for large computational grids.
• In this work, we apply Fourier Neural Operator (FNO) networks as a fast data-driven deep learning method for solving the 2D photoacoustic wave equation in a homogeneous medium.
• Comparisons between the FNO network and pseudo-spectral time domain approach demonstrated that the FNO network generated comparable simulations with small errors and was several orders of magnitude faster.