September 19, 2024
Conference Paper

A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models

Abstract

Computer vision-based analysis of micrographs of nuclear materials is an emerging technique for property prediction, synthetic route identification, and other material analysis tasks. These analysis tasks play a pivotal role in many material characterization applications such as signature development for treaty verification, process optimization, etc. The backbone in many of the recent computer vision-based techniques is a deep learning model, which takes a fixed-size set of pixels and provides a class prediction for that set of pixels. For example, previous work developed a deep convolutional neural network (CNN) to predict the synthetic route from a 256 px x 256 px patch taken from a larger image of uranium ore concentrates. In this work, we present several methods for first calibrating these models in a manner that they can provide accurate probabilities of their predictions’ veracity, and several methods of combining these probabilities. Overall, the combination of these two steps into a pipeline allows for full-image and even full-sample (where a sample has many images) predictions with associated confidence values. Finally, we show that one can also use the patch predictions and confidence to produce a visualization to map predicted constituents through the image. Results and examples for predicting and mapping uranium ore concentrates’ synthetic process from imagery will be presented.

Published: September 19, 2024

Citation

Hagen A.R., S.W. Jackson, J.L. Yaros, N.H. Ly, and C.A. Nizinski. 2024. A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models. In Proceedings of the 65th Annual Meeting of the Institute of Nuclear Materials Management, July 21-25, 2024, Portland, OR, 1-12. Mount Laurel, New Jersey:Institute of Nuclear Materials Management. PNNL-SA-200177.