Data-enabled Fusion Technology (DeFT): Machine Learning Tools in the Ousai Platform
ORAL
Abstract
This talk provides an overview of the Ousai framework—a framework facilitating cutting edge application of machine learning, data analytics, and AI to problems in fusion and beyond. The three categories of machine learning/AI tool types utilized in the Ousai framework are: analysis tools, optimization tools, and anomaly detectors. Our suite of analysis tools includes system identification and regression tools, model discovery and extraction tools, and model enhancement tools. Our suite of optimization tools include spectroscopy tools, configuration performance tools, and machine performance enhancement tools. Our suite of anomaly detectors include performance evaluation tools and contaminant detectors. We will discuss some highlights from the Ousai platform of machine learning tools for data-enabled fusion technology in this talk.
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Publication: A Gaussian Process Guide for Signal Regression in Nuclear Fusion, Craig Michoski, Todd Oliver, David Hatch, Ahmed Diallo, Mike Kotschenreuther, David Eldon, Rich Groebner, Oak Nelson (In Preparation)<br><br>System Architectures for Integrated Analysis (SAFIA), (In Preparation), Craig Michoski, Todd Oliver, David Hatch, Dongyang Kuang, Steph-Y. Louis, Siwei Luo, Matthieu Vitse
Presenters
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Craig Michoski
University of Texas at Austin
Authors
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Craig Michoski
University of Texas at Austin
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David R Hatch
University of Texas at Austin, Institute for Fusion Studies, University of Texas at Austin
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Todd Oliver
ODEN Institute
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Dongyang Kuang
University of Texas at Austin
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Steph-Y. Louis
Sapientai LLC
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Siwei Luo
Sapientai LLC
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Matthieu Vitse
Sapientai LLC