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[2602.15890] Surrogate Modeling for Neutron Transport: A Neural Operator Approach
Summary
This article presents a neural operator framework for surrogate modeling in neutron transport, demonstrating significant computational efficiency and predictive accuracy compared to traditional methods.
Why It Matters
The development of surrogate models for neutron transport is crucial for enhancing computational efficiency in physics simulations. This research leverages advanced machine learning techniques to provide faster and more accurate predictions, which can significantly impact fields such as nuclear engineering and real-time digital twin applications.
Key Takeaways
- Introduces a neural operator framework for neutron transport computations.
- Demonstrates that Fourier Neural Operator (FNO) outperforms Deep Operator Network (DeepONet) in predictive accuracy.
- Both models achieve substantial speedups, requiring less than 0.3% of the runtime of conventional solvers.
- Surrogate models can replace intensive transport sweep loops, enhancing computational efficiency.
- Results indicate potential applications in real-time digital twin technologies and design optimization.
Physics > Computational Physics arXiv:2602.15890 (physics) [Submitted on 7 Feb 2026] Title:Surrogate Modeling for Neutron Transport: A Neural Operator Approach Authors:Md Hossain Sahadath, Qiyun Cheng, Shaowu Pan, Wei Ji View a PDF of the paper titled Surrogate Modeling for Neutron Transport: A Neural Operator Approach, by Md Hossain Sahadath and 3 other authors View PDF Abstract:This work introduces a neural operator based surrogate modeling framework for neutron transport computation. Two architectures, the Deep Operator Network (DeepONet) and the Fourier Neural Operator (FNO), were trained for fixed source problems to learn the mapping from anisotropic neutron sources, Q(x,{\mu}), to the corresponding angular fluxes, {\psi}(x,{\mu}), in a one-dimensional slab geometry. Three distinct models were trained for each neural operator, corresponding to different scattering ratios (c = 0.1, 0.5, & 1.0), providing insight into their performance across distinct transport regimes (absorption-dominated, moderate, and scattering-dominated). The models were subsequently evaluated on a wide range of previously unseen source configurations, demonstrating that FNO generally achieves higher predictive accuracy, while DeepONet offers greater computational efficiency. Both models offered significant speedups that become increasingly pronounced as the scattering ratio increases, requiring <0.3% of the runtime of a conventional S_N solver. The surrogate models were further incorporated into ...