An ML/AI Approach to Identifying Gaps in a priori
Understanding of Nuclear Facility Design and Operations

D. Rosa de Jesus - Pacific Northwest National Laboratory
Lee Burke - Pacific Northwest National Laboratory
Carlos Gonzalez Rivera - Pacific Northwest National Laboratory
Jackson Chin - Pacific Northwest National Laboratory
Jereme Haack - Pacific Northwest National Laboratory
Romarie Morales Rosado - Pacific Northwest National Laboratory
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Monitoring and characterization of nuclear facilities is an essential activity of nuclear nonproliferation, materials control, and safeguards. Such inferences are best supported by extensive a priori knowledge of facility design and operations, but that knowledge is not always correct, current, and complete. We present a technique for identifying discrepancies between a priori understanding and actual conditions on the ground by comparing the output of computational models of facility activities with sensor data gathered on-site. The technique leverages a novel unsupervised machine learning algorithm to provide a near-real-time rating of the discrepancy between expected and observed behavior. The algorithm is validated against a comprehensive anomaly detection benchmark, including 14 other unsupervised anomaly detection methods on ten datasets. We present promising results from applying the proposed technique to a prototype ML/AI system deployed at two testbed facilities. The results show the algorithm's effectiveness in identifying and explaining real-world discrepancies in support of monitoring and characterization activities.