Speaker
Description
Radiative transfer is a fundamental ingredient of computational astrophysics, essential both for interpreting observations and for modeling the thermal and dynamical impact of radiation on astrophysical systems. Yet accurate radiative transfer remains one of the main computational bottlenecks in modern simulations. Standard approaches such as ray tracing and Monte Carlo methods are powerful and widely used, but they are also expensive: ray-tracing schemes can scale poorly with the number of sources and are often difficult to parallelize efficiently, while Monte Carlo methods require large numbers of photon packets to reduce stochastic noise. When radiative transfer is coupled to hydrodynamics, the problem becomes even more demanding because of the non-local interaction between radiation and matter and the large disparity between the speed of light and typical gas velocities. In this work, we present a neural-operator-based surrogate model for accelerating three-dimensional, monochromatic, time-dependent radiative transfer in the absorption-emission regime. Our method combines a Fourier Neural Operator with U-Net components to capture both global transport patterns and localized structures in the radiation field. Trained on numerical solutions, the model predicts the temporal evolution of radiative intensity from the current radiation field together with the absorption and emission distributions. We show that the surrogate reproduces reference solutions with an average relative error below 3% while achieving speedups of more than two orders of magnitude, demonstrating its potential as an efficient and accurate emulator for next-generation radiation-hydrodynamic simulations.