Application of Deep Operator Networks for Data Reduction in Deterministic Neutron Calculations
Ben Whewell, Ryan G. McClarren, Nathan Gibson, and Ajeeta Khatiwada, (2026). Under Review. engrXiv: 10.31224/8581
Deep operator networks for learned surrogates and model compression.
Preprint
@misc{whewell2026deeponet,
author = {Whewell, Ben and McClarren, Ryan G. and Gibson, Nathan and Khatiwada, Ajeeta},
title = {{Application of Deep Operator Networks for Data Reduction in Deterministic Neutron Calculations}},
year = {2026},
howpublished = {engrXiv},
doi = {10.31224/8581},
note = {Under review}
}
Joshua Nichols, Andrew Osborne, Ben Whewell, and Nathan Gibson, (2026). arXiv: 2610.10464
Artificial neural networks for surrogate regression modeling.
Preprint
@misc{nichols2026targeted,
author = {Nichols, Joshua and Osborne, Andrew and Whewell, Ben and Gibson, Nathan},
title = {{Self-Shielded Multigroup Microscopic Cross Section Generation Using Targeted Artificial Neural Networks}},
year = {2026},
eprint = {2610.10464},
archivePrefix = {arXiv}
}
Application of Reinforcement Learning for Multigroup Energy Grid Optimization for Neutron Transport Criticality Problems
Ben Whewell, Nathan Gibson, and Ajeeta Khatiwada, (2026). Under Review. arXiv: 2605.27895
Deep reinforcement learning for sequential decision-making and combinatorial optimization.
Preprint
@misc{whewell2026rlgrid,
author = {Whewell, Ben and Gibson, Nathan and Khatiwada, Ajeeta},
title = {{Application of Reinforcement Learning for Multigroup Energy Grid Optimization for Neutron Transport Criticality Problems}},
year = {2026},
eprint = {2605.27895},
archivePrefix = {arXiv},
note = {Under review}
}
Application of Reinforcement Learning to Multigroup Energy Grid Optimization for Criticality Problems
Ben Whewell, Nathan Gibson, and Ajeeta Khatiwada, (2026). International Conference on the Physics of Reactors (PHYSOR 2026), Turin, Italy.
Deep reinforcement learning for sequential decision-making and combinatorial optimization.
@inproceedings{whewell2026physor,
author = {Whewell, Ben and Gibson, Nathan and Khatiwada, Ajeeta},
title = {{Application of Reinforcement Learning to Multigroup Energy Grid Optimization for Criticality Problems}},
booktitle = {International Conference on the Physics of Reactors (PHYSOR 2026)},
address = {Turin, Italy},
year = {2026}
}
Predicting Self-Shielded Multigroup Microscopic Cross Sections Using Artificial Neural Networks
Joshua Nichols, Ben Whewell, Nathan Gibson, and Andrew Osborne, (2026). International Conference on the Physics of Reactors (PHYSOR 2026), Turin, Italy.
Artificial neural networks for surrogate regression modeling.
@inproceedings{nichols2026shielding,
author = {Nichols, Joshua and Whewell, Ben and Gibson, Nathan and Osborne, Andrew},
title = {{Predicting Self-Shielded Multigroup Microscopic Cross Sections Using Artificial Neural Networks}},
booktitle = {International Conference on the Physics of Reactors (PHYSOR 2026)},
address = {Turin, Italy},
year = {2026}
}
2025
MetaHeuristic Feature Selection for Energy Group Optimization and Analysis
Natalie Rouse, Benjamin Whewell, and Nathan Gibson, (2025). Technical Report LA-UR-25-29285, Los Alamos National Laboratory. DOI: 10.2172/2588817
Metaheuristic feature selection for high-dimensional optimization.
Report
@techreport{rouse2025metaheuristic,
author = {Rouse, Natalie and Whewell, Benjamin and Gibson, Nathan},
title = {{MetaHeuristic Feature Selection for Energy Group Optimization and Analysis}},
institution = {Los Alamos National Laboratory},
year = {2025},
number = {LA-UR-25-29285},
doi = {10.2172/2588817}
}
Single Grid Error Estimation for Neutron Transport Solvers
Ben Whewell and Ryan G. McClarren, (2025). Journal of Verification, Validation and Uncertainty Quantification. 10(2): 021001. DOI: 10.1115/1.4069426
@article{whewell2025vvuq,
author = {Whewell, Ben and McClarren, Ryan G.},
title = {{Single Grid Error Estimation for Neutron Transport Solvers}},
journal = {Journal of Verification, Validation and Uncertainty Quantification},
year = {2025},
volume = {10},
number = {2},
pages = {021001},
doi = {10.1115/1.4069426}
}
Collision-Based Hybrid Method for Two-Dimensional Neutron Transport Problems
Ben Whewell and Ryan G. McClarren, (2025). Nuclear Science and Engineering, 1-23. DOI: 10.1080/00295639.2025.2489778
@article{whewell2025hybrid2d,
author = {Whewell, Ben and McClarren, Ryan G.},
title = {{Collision-Based Hybrid Method for Two-Dimensional Neutron Transport Problems}},
journal = {Nuclear Science and Engineering},
year = {2025},
pages = {1--23},
doi = {10.1080/00295639.2025.2489778}
}
Single Grid Error Estimate in Two-Dimensional Neutron Transport Problems
Ben Whewell and Ryan G. McClarren, (2025). The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering.
@inproceedings{whewell2025mcsinglegrid,
author = {Whewell, Ben and McClarren, Ryan G.},
title = {{Single Grid Error Estimate in Two-Dimensional Neutron Transport Problems}},
booktitle = {The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering},
year = {2025}
}
Single Grid Error Estimate in Two-Dimensional Neutron Transport Problems
Ben Whewell and Ryan G. McClarren, (2025). The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering. Technical Session.
@misc{whewell2025mcsinglegrid-talk,
author = {Whewell, Ben and McClarren, Ryan G.},
title = {{Single Grid Error Estimate in Two-Dimensional Neutron Transport Problems}},
year = {2025},
howpublished = {Technical session, The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering}
}
2024
Hybrid Numerical Methods and Solution Verification for the Neutron Transport Equation
Benjamin Joseph Whewell, (2024). University of Notre Dame. Dissertation. DOI: 10.7274/27927858.v1
Report
@phdthesis{whewell2024dissertation,
author = {Whewell, Benjamin Joseph},
title = {{Hybrid Numerical Methods and Solution Verification for the Neutron Transport Equation}},
school = {University of Notre Dame},
year = {2024},
doi = {10.7274/27927858.v1}
}
2023
Solution Verification with the Method of Nearby Problems for Neutron Transport Applications
Ben Whewell and Ryan G. McClarren, (2023). The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering.
@inproceedings{whewell2023nearby,
author = {Whewell, Ben and McClarren, Ryan G.},
title = {{Solution Verification with the Method of Nearby Problems for Neutron Transport Applications}},
booktitle = {The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering},
year = {2023}
}
Solution Verification with the Method of Nearby Problems for Neutron Transport Applications
Ben Whewell and Ryan G. McClarren, (2023). The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering. Poster Presentation.
@misc{whewell2023nearby-poster,
author = {Whewell, Ben and McClarren, Ryan G.},
title = {{Solution Verification with the Method of Nearby Problems for Neutron Transport Applications}},
year = {2023},
howpublished = {Poster presentation, The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering}
}
Multigroup Neutron Transport Using a Collision-Based Hybrid Method
Ben Whewell, Ryan G. McClarren, Cory D. Hauck, and Minwoo Shin, (2023). Nuclear Science and Engineering, 197:7, 1386-1405. DOI: 10.1080/00295639.2022.2154119
@article{whewell2023multigroup,
author = {Whewell, Ben and McClarren, Ryan G. and Hauck, Cory D. and Shin, Minwoo},
title = {{Multigroup Neutron Transport Using a Collision-Based Hybrid Method}},
journal = {Nuclear Science and Engineering},
year = {2023},
volume = {197},
number = {7},
pages = {1386--1405},
doi = {10.1080/00295639.2022.2154119}
}
2022
A low-rank power iteration scheme for neutron transport criticality problems
Jonas Kusch, Benjamin Whewell, Ryan McClarren, and Martin Frank, (2022). Journal of Computational Physics, 470, 111587. DOI: 10.1016/j.jcp.2022.111587
Journal
@article{kusch2022lowrank,
author = {Kusch, Jonas and Whewell, Benjamin and McClarren, Ryan G. and Frank, Martin},
title = {{A low-rank power iteration scheme for neutron transport criticality problems}},
journal = {Journal of Computational Physics},
year = {2022},
volume = {470},
pages = {111587},
doi = {10.1016/j.jcp.2022.111587}
}
Data Reduction in Deterministic Neutron Transport Calculations Using Machine Learning
Ben Whewell and Ryan G. McClarren, (2022). Annals of Nuclear Energy, 176, p. 109276. DOI: 10.1016/j.anucene.2022.109276
Neural-network autoencoders for dimensionality reduction and model compression.
@article{whewell2022datareduction,
author = {Whewell, Ben and McClarren, Ryan G.},
title = {{Data Reduction in Deterministic Neutron Transport Calculations Using Machine Learning}},
journal = {Annals of Nuclear Energy},
year = {2022},
volume = {176},
pages = {109276},
doi = {10.1016/j.anucene.2022.109276}
}
2021
Reduced Models for Nuclear Data in Transport Equations
B. Whewell and Ryan G. McClarren, (2021). The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, p. 1427-1436.
Dimensionality reduction and reduced-order modeling.
@inproceedings{whewell2021reduced,
author = {Whewell, Ben and McClarren, Ryan G.},
title = {{Reduced Models for Nuclear Data in Transport Equations}},
booktitle = {The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering},
year = {2021},
pages = {1427--1436}
}
Reduced Models for Nuclear Data in Transport Equations
B. Whewell and Ryan G. McClarren, (2021). Society for Industrial and Applied Mathematics Conference on Computational Science and Engineering. Virtual.
Dimensionality reduction and reduced-order modeling.
@misc{whewell2021reduced-talk,
author = {Whewell, Ben and McClarren, Ryan G.},
title = {{Reduced Models for Nuclear Data in Transport Equations}},
year = {2021},
howpublished = {Society for Industrial and Applied Mathematics Conference on Computational Science and Engineering, virtual}
}
2020
Evaluating 239Pu(n,f) cross sections via machine learning using experimental data, covariances, and measurement features
B. Whewell, M. Grosskopf, and D. Neudecker, (2020). Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment, 978, p. 164305. DOI: 10.1016/j.nima.2020.164305
Supervised ML regression with feature engineering on experimental data and covariances.
@article{whewell2020pu239,
author = {Whewell, Ben and Grosskopf, Michael and Neudecker, Denise},
title = {{Evaluating 239Pu(n,f) cross sections via machine learning using experimental data, covariances, and measurement features}},
journal = {Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment},
year = {2020},
volume = {978},
pages = {164305},
doi = {10.1016/j.nima.2020.164305}
}
Applying Autoencoders for Data Reduction of Neutron Scattering Matrices
Ben Whewell and Ryan G. McClarren, (2020). Conference on Data Analysis. Poster Presentation.
Autoencoder representation learning for high-dimensional data compression.
@misc{whewell2020autoencoders,
author = {Whewell, Ben and McClarren, Ryan G.},
title = {{Applying Autoencoders for Data Reduction of Neutron Scattering Matrices}},
year = {2020},
howpublished = {Poster presentation, Conference on Data Analysis}
}
2019
Data Fusion Techniques for Improving Fission Neutron Multiplicity Data
Benjamin Whewell, Ryan G. McClarren, and Simon Bolding, (2019). The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, p. 1072-1080.
Statistical data fusion for multi-source data integration.
@inproceedings{whewell2019datafusion,
author = {Whewell, Benjamin and McClarren, Ryan G. and Bolding, Simon},
title = {{Data Fusion Techniques for Improving Fission Neutron Multiplicity Data}},
booktitle = {The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering},
year = {2019},
pages = {1072--1080}
}
Adjusting for Outlying Observations in Nuclear Data Evaluation using Machine Learning
Benjamin Whewell, (2019). Technical Report LA-UR-19-26924, Los Alamos National Laboratory.
ML-based anomaly and outlier detection for data quality.
@techreport{whewell2019outliers,
author = {Whewell, Benjamin},
title = {{Adjusting for Outlying Observations in Nuclear Data Evaluation using Machine Learning}},
institution = {Los Alamos National Laboratory},
year = {2019},
number = {LA-UR-19-26924}
}
