In this study, we propose a novel neural network approach, termed DeepRTE, to address the steady-state Radiative Transfer Equation (RTE). The RTE is a differential-integral equation that governs the propagation of radiation through a participating medium, with applications spanning diverse domains such as neutron transport, atmospheric radiative transfer, heat transfer, and optical imaging. Our proposed DeepRTE framework leverages pre-trained attention-based neural networks to solve the RTE with high accuracy and computational efficiency. The efficacy of the proposed approach is substantiated through comprehensive numerical experiments.
View on arXiv@article{zhu2025_2505.23190,
title={ DeepRTE: Pre-trained Attention-based Neural Network for Radiative Tranfer },
author={ Yekun Zhu and Min Tang and Zheng Ma },
journal={arXiv preprint arXiv:2505.23190},
year={ 2025 }
}