CTF4Nuclear: a Common Task Framework for Scientific Machine Learning in Nuclear Systems

Designing, monitoring, and operating nuclear systems is exceptionally challenging because of the complex physical phenomena and their interactions that govern the system dynamics. While high-fidelity simulations can resolve these non-linear, multi-physics interactions (e.g., between neutronics and thermal-hydraulics), they are computationally expensive and rarely suitable for multi-query and real-time applications. Furthermore, model-based approaches inherently rely on simplifying assumptions, leading to inevitable discrepancies with real-world measurements. Machine Learning (ML) surrogates offer a promising alternative; however, systematic comparisons of their performance across relevant operating conditions and data constraints remain limited. In safety-critical settings such as nuclear engineering, a fair and rigorous comparison of different ML methods, and a clear understanding of their advantages and limitations, is of paramount importance. To address this gap, we introduce a Common Task Framework (CTF) for the application of ML in nuclear engineering, building upon previous efforts in dynamical systems and seismology. This CTF considers a curated set of datasets spanning different nuclear and nuclear-adjacent systems, including molten salt, micro-reactors, electrically conducting fusion coolants, and experimental thermal-hydraulics facilities. To demonstrate the rigour and fairness of the CTF, we apply it to the one of the most strongly coupled system, the molten salt reactor, evaluating the performance of all methods on twelve established metrics (forecasting, noise robustness, limited data, and parametric generalisation), and identifying substantial differences in their robustness and generalisation. Additionally, we introduce a sparse-observation monitoring paradigm to evaluate whether models can reconstruct and forecast system dynamics from limited sensor measurements, providing a common setting for comparing forecasting and state-estimation methods. Overall, the proposed CTF provides a reproducible basis for systematic comparison of data-driven dynamical models in nuclear engineering and establishes a framework for assessing ML capability under realistic data and observation constraints in complex, safety-critical physical systems.