CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
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2026
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| author | Riva, Stefano Introini, Carolina Cammi, Antonio Price, Dean Yermakov, Alexey Zhao, Yue Wyder, Philippe M. Goldfeder, Judah Williams, Jan Rude, Amy Sara Tomasetto, Matteo Germany, Joe Bakarji, Joseph Maierhofer, Georg Cranmer, Miles Kutz, J. Nathan |
| author_facet | Riva, Stefano Introini, Carolina Cammi, Antonio Price, Dean Yermakov, Alexey Zhao, Yue Wyder, Philippe M. Goldfeder, Judah Williams, Jan Rude, Amy Sara Tomasetto, Matteo Germany, Joe Bakarji, Joseph Maierhofer, Georg Cranmer, Miles Kutz, J. Nathan |
| contents | The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, designing and operating these systems is exceptionally difficult, given the complexity of the physical phenomena that interact to form the system dynamics. While high-fidelity simulations help to understand the non-linear, multi-physics interactions within a reactor, they are computationally expensive and rarely suitable for real-time applications. Furthermore, model-based approaches are inherently sensitive to simplifying assumptions required to derive their governing equations and parameters, leading to inevitable discrepancies with real-world measurements. In contrast, Machine Learning (ML) methods have the potential to generate reliable surrogate models which may be able to quickly predict the system's behaviour. However, the number of data-driven methods that can potentially be used for this task is large and diverse. In a safety-critical setting such as nuclear engineering, a fair comparison of different ML methods, and a clear understanding of their advantages and limitations, is of paramount importance. To address this, we introduce a Common Task Framework (CTF) for ML in nuclear engineering, building upon previous efforts in dynamical systems and seismology. This CTF considers a curated set of datasets from different nuclear and nuclear-adjacent systems. The CTF evaluates the performance of a method on 12 established metrics, alongside a new paradigm focused on system monitoring from sparse measurements only. We illustrate the framework by benchmarking standard ML baselines against these datasets, revealing current method limitations. Our vision is to replace ad hoc comparisons with standardized evaluations on hidden test sets, raising the bar for rigour and reproducibility in scientific ML for the nuclear industry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15549 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models Riva, Stefano Introini, Carolina Cammi, Antonio Price, Dean Yermakov, Alexey Zhao, Yue Wyder, Philippe M. Goldfeder, Judah Williams, Jan Rude, Amy Sara Tomasetto, Matteo Germany, Joe Bakarji, Joseph Maierhofer, Georg Cranmer, Miles Kutz, J. Nathan Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, designing and operating these systems is exceptionally difficult, given the complexity of the physical phenomena that interact to form the system dynamics. While high-fidelity simulations help to understand the non-linear, multi-physics interactions within a reactor, they are computationally expensive and rarely suitable for real-time applications. Furthermore, model-based approaches are inherently sensitive to simplifying assumptions required to derive their governing equations and parameters, leading to inevitable discrepancies with real-world measurements. In contrast, Machine Learning (ML) methods have the potential to generate reliable surrogate models which may be able to quickly predict the system's behaviour. However, the number of data-driven methods that can potentially be used for this task is large and diverse. In a safety-critical setting such as nuclear engineering, a fair comparison of different ML methods, and a clear understanding of their advantages and limitations, is of paramount importance. To address this, we introduce a Common Task Framework (CTF) for ML in nuclear engineering, building upon previous efforts in dynamical systems and seismology. This CTF considers a curated set of datasets from different nuclear and nuclear-adjacent systems. The CTF evaluates the performance of a method on 12 established metrics, alongside a new paradigm focused on system monitoring from sparse measurements only. We illustrate the framework by benchmarking standard ML baselines against these datasets, revealing current method limitations. Our vision is to replace ad hoc comparisons with standardized evaluations on hidden test sets, raising the bar for rigour and reproducibility in scientific ML for the nuclear industry. |
| title | CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2605.15549 |