A Fourier Neural Operator for Accelerated Discretization-Invariant Solutions of the Radiative Transfer Equation

Abstract

The radiative transfer equation (RTE) governs thermal radiation in participating media, which is critical to modeling combustion, atmospheric, high-temperature, and radiative thermal management applications. However, due to the inherently high-dimensional nature of radiative transfer, numerical solutions to the RTE induce significant computational cost. This work develops a Fourier neural operator (FNO) as a fast, discretization-invariant, surrogate model for predicting the absorbed heat flux field in participating media. A dataset is generated on a benchmark 2-dimensional problem modeling thermal surface emission into a participating medium with spatially varying properties using Monte Carlo simulations. The FNO is trained to learn the solution operator, mapping the input property fields and geometry to the resulting heat flux field. The trained FNO provides up to 26-times computational acceleration compared to Monte Carlo simulation at equivalent error. Furthermore, as the FNO learns the solution operator and not an image-to-image mapping, the trained FNO accurately generalizes to various spatial discretizations, including those not seen in the training dataset. A spectral analysis of the spatial frequencies present in the heat flux prediction demonstrates the FNO's ability to suppress the high-frequency noise present in the training dataset, while preserving the physically meaningful low-mid frequency spectrum. These results demonstrate the Fourier neural operator surrogate model can provide accurate, computationally efficient, and discretization-invariant predictions for radiative transfer in a participating medium. By learning the underlying solution operator, this approach moves toward the development of surrogate models for radiative transfer capable of generalizing across a broad range of geometries, discretizations, and boundary conditions.