# Machine learning-assisted surrogate construction for full-core fuel performance analysis

arXiv: https://arxiv.org/abs/2104.09499 · HTML: https://mlsanity.com/paper/2104.09499/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Understanding Random Forests: From Theory to Practice](https://mlsanity.com/paper/1407.7502/) (2014)

## Built on by (most cited first, 6 of 6)

- [Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks](https://mlsanity.com/paper/2407.04726/) (2024)
- [Artificial Intelligence in Reactor Physics: Current Status and Future Prospects](https://mlsanity.com/paper/2503.02440/) (2025)
- [Techno-economic optimization of a heat-pipe microreactor, part I: theory and cost optimization](https://mlsanity.com/paper/2512.16032/) (2025)
- [Multifidelity Surrogate Modeling of Depressurized Loss of Forced Cooling in High-temperature Gas Reactors](https://mlsanity.com/paper/2603.14143/) (2026)
- [Scientific feedback reveals productive regimes beyond training conventions](https://mlsanity.com/paper/2512.15756/) (2025)
- [MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications](https://mlsanity.com/paper/2604.26973/) (2026)

## Abstract (arXiv)

Accurately predicting the behavior of a nuclear reactor requires multiphysics simulation of coupled neutronics, thermal-hydraulics and fuel thermo-mechanics. The fuel thermo-mechanical response provides essential information for operational limits and safety analysis. Traditionally, fuel performance analysis is performed standalone, using calculated spatial-temporal power distribution and thermal boundary conditions from the coupled neutronics-thermal-hydraulics simulation as input. Such one-way coupling is result of the high cost induced by the full-core fuel performance analysis, which provides more realistic and accurate prediction of the core-wide response than the "peak rod" analysis. It is therefore desirable to improve the computational efficiency of full-core fuel performance modeling by constructing fast-running surrogate, such that fuel performance modeling can be utilized in the core reload design optimization. This work presents methodologies for full-core surrogate construction based on several realistic equilibrium PWR core designs. As a fast and conventional approach, look-up tables are only effective for certain fuel performance quantities of interest (QoIs). Several representative machine-learning algorithms are introduced to capture the complicated physics for other fuel performance QoIs. Rule-based model is useful as a feature extraction technique to account for the spatial-temporal complexity of operating conditions. Constructed surrogates achieve at least ten thousand time acceleration with satisfying prediction accuracy. Current work lays foundation for tighter coupling of fuel performance modeling into the core design optimization framework. It also sets stage for full-core fuel performance analysis with BISON where the computational cost becomes more burdensome.