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

Vanquishing the computational cost of passive gamma emission tomography simulations: a physics-aware reduced order modeling approach