Author ORCID Identifier
Document Type
Poster
Publication Date
Summer 8-3-2026
Abstract
Advancements in technology have enabled scientists and engineers in the synchrotron science domain to generate high-dimensional data. To conduct a machine learning study on these data, an analyst undergoes multiple analytical stages, including dimensionality reduction, predictive modelling, and uncertainty quantification (UQ).
To ensure reproducibility in science, it is necessary to document the semantic relationships among the stages of the analysis to enable provenance, interoperability, and knowledge reuse. In this work, we present a Semantic Data Management (SDM) framework rooted in FAIR principles that provides ontologies for UQ workflows. We illustrate the application of UQ ontology in synchrotron diffraction pattern analyses that support synchrotron-based materials characterization.
Specifically, the SDM was applied to studies that investigated the UQ capabilities of Principal Component-Enabled Gaussian Process Regression and Deep Kernel Learning models for predicting β-phase volume fraction from synchrotron X-ray diffraction patterns of Ti–6Al–4V alloy obtained during heat treatment at different feature configuration settings.
Keywords
semantic data management, ontologies, uncertainty quantification, Gaussian process regression, deep kernel learning
Language
English
Publication Title
12th Annual Data Science in Engineering and Life Sciences Symposium
Grant
DE-NA0004104
Funder
U.S. Department of Energy's National Nuclear Security Administration under Award Number(s) DE-NA0004104.
Rights
© The Author(s). This is an Open Access work distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Olatunde, Ayorinde E.; Dernek, Ozan; Barcelos, Erika I.; French, Roger H.; and Mondal, Anirban, "A Semantic Data Management Framework for Uncertainty Quantification in Synchrotron Diffraction Pattern Analysis" (2026). Student Scholarship. 690.
https://commons.case.edu/studentworks/690