Nested Fourier-enhanced neural operator for efficient modeling of radiation transfer in fires
Computational fluid dynamics (CFD) has become an essential tool for predicting fire behavior, yet maintaining both efficiency and accuracy remains challenging. A major source of computational cost in fire simulations is the modeling of radiation transfer, which is usually the dominant heat transfer mechanism in fires. Solving the high-dimensional radiative transfer equation (RTE) with traditional numerical methods can be a performance bottleneck. Here, we present a machine learning framework based on Fourier-enhanced multiple-input neural operators (Fourier-MIONet) as an efficient alternative to direct numerical integration of the RTE. We first investigate the performance of neural operat...
| Authors | Anran Jiao, Wengyao Jiang, Xiaoyi Lu et al. |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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