Neural Operator Surrogates for Two-Dimensional Neutron Flux Estimation
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Computer Science > Machine Learning
Title:Neural Operator Surrogates for Two-Dimensional Neutron Flux Estimation
Abstract:This work extends our one-dimensional single-sweep neural-operator studies to two dimensions. We consider one-group transport with isotropic scattering. As in the one-dimensional work, we use Fourier neural operators (FNOs) to approximate the high-fidelity scalar flux. Additionally, we also investigate U-shaped neural operators (UNOs) in this study. We consider three surrogates. The first two map the material and source fields directly to the flux, one using an FNO and one using a UNO. The third is an FNO that additionally takes the scalar flux after one source iteration, the single-sweep approximation, as an input. Each case is solved to high fidelity with a verified discrete-ordinates solver, and an average relative L_2 error norm is used to characterize the quality of the inferred maps. We train every surrogate over three random seeds so that differences between them can be assessed against run-to-run variability. Two questions guide the study: whether the single-sweep input improves accuracy over the direct maps, and whether training on the logarithm of the flux improves accuracy in the strongly attenuated regions relevant to shielding.
| Comments: | to be submitted to ANS Winter 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.19388 [cs.LG] |
| (or arXiv:2607.19388v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.19388
arXiv-issued DOI via DataCite (pending registration)
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