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.

翻译：清洁能源的需求日益增长，新型核技术为可再生能源提供了互补解决方案。然而，鉴于构成系统动力学的物理现象之间相互作用的复杂性，设计并运行此类系统极其困难。尽管高保真模拟有助于理解反应堆内非线性、多物理场耦合作用，但其计算成本高昂，且极少适用于实时场景。此外，基于模型的方法本质上依赖于推导其控制方程与参数时所作的简化假设，这必然导致与实际测量结果存在偏差。相比之下，机器学习方法有望生成可靠的替代模型，能够快速预测系统行为。然而，可应用于此任务的数据驱动方法种类繁多且差异显著。在核工程这类安全关键领域，公平比较不同机器学习方法并清晰理解其优势与局限性至关重要。为此，我们基于先前在动力系统与地震学领域的工作，为核工程中的机器学习引入了一个通用任务框架。该框架整合了来自不同核系统及核相关系统的精选数据集，在12项公认指标以及一种仅基于稀疏测量进行系统监测的新范式上评估方法性能。我们通过对标准机器学习基线方法进行基准测试来展示该框架，揭示了当前方法的局限性。我们的愿景是：以隐藏测试集上的标准化评估取代临时性对比，从而提升核工业领域科学机器学习研究的严格性与可复现性标准。