MPEX AI Digital Twins
Gary Staebler, Rhea Barnett, Mark Cianciosa, Rinkle Juneja, Atul Kumar, Wouter Tierens, Minglei Yang, Cory Hauck, Richard Archibald, Pablo Seleson, Sam Reeve, Ben Dudson, Vasily Geyko
TL;DR
The MPEX AI Digital Twins project aims to create AI models trained on simulation data to enhance material assessment and operational control for the MPEX device.
Contribution
It introduces a framework for training AI digital twins using experimental and physics simulation data for materials testing.
Findings
Development of AI digital twins for MPEX materials assessment.
Use of physics and experimental data to train AI models.
Enhanced data processing and analysis capabilities for MPEX.
Abstract
Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project
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