To enable future innovative designs for fusion platforms on the Z machine, a Sandia LDRD team developed an automated framework for constructing material models. This directly addressed the challenge of creating comprehensive material models that can leverage machine learning and integrate data from multi-fidelity datasets, something vital for uncertainty quantification analyses of magnetohydrodynamic codes. This project, which included collaborations with Los Alamos and Lawrence Livermore national laboratories and multiple universities, was presented on at IEEE International Conference on Plasma Science and the American Physical Society Division of Plasma Physics. Future projects will explore additional materials of interest to the NNSA and leverage machine learning frameworks for model refinement.
Sandia experts linked to work
- Lucas Stanek
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AIAA Journal
July 14, 2025