Passive Gamma Emission Tomography (PGET) has been developed by the International Atomic Energy Agency as a way to directly image the spatial distribution of individual fuel pins in a spent nuclear fuel assembly and so determine potential diversion. Constructing the analysis and interpretation of PGET measurements rely on the availability of comprehensive datasets. Experimental data are expensive, limited, and so are augmented by Monte Carlo simulations. The main issue concerning Monte Carlo simulations is the high computational cost to simulate the 360 angular views of the tomography. Similar challenges pervade numerical science. To address this challenge, we have developed a physics-aware reduced order modeling approach. It provides a framework to combine a small subset of the 360 angular views with a computationally inexpensive proxy solution, that brings the essence of the physics, to obtain a real-time high-fidelity solution at all angular views, but at a fraction of the computational cost.

翻译：国际原子能机构开发了无源伽马发射断层扫描（PGET）技术，用于直接成像乏燃料组件中单个燃料棒的空间分布，从而判定潜在的核材料滥用。构建PGET测量的分析与解读依赖于综合性数据集的可用性。实验数据成本高昂且有限，因此通常通过蒙特卡罗模拟进行补充。蒙特卡罗模拟的主要问题在于，模拟断层扫描的360个角度视图需要极高的计算成本。类似的挑战普遍存在于数值科学领域。为解决这一难题，我们提出了一种物理感知降阶建模方法。该方法提供了一个框架，通过将360个角度视图中的一小部分子集与计算成本低廉的代理解（该解蕴含物理本质）相结合，能够以极低的计算成本实时获得所有角度视图的高保真解。