Papers › DeepRTE: Pre-trained Attention-based Neural Network for Radiative Tranfer
DeepRTE: Pre-trained Attention-based Neural Network for Radiative Tranfer
Yekun Zhu, Min Tang, Zheng Ma
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.
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