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Prediction of heat transport in multiple tokamak devices with neural networks

Abstract The OMFIT (One Modeling Framework for Integrated Tasks) modeling environment and the BRAINFUSE module have been deployed on the PPPL (Princeton Plasma Physics Laboratory) computing cluster with modifications that have rendered the application of artificial neural networks (NNs) to the TRANSP databases for the JET (Joint European Torus), TFTR (Tokamak Fusion Test Reactor), and NSTX (National Spherical Torus Experiment) devices possible through their use. This development has facilitated the investigation of NNs for predicting heat transport profiles in JET, TFTR, and NSTX, and has promoted additional investigations to discover how else NNs may be of use to scientists at PPPL. In applying NNs to the aforementioned devices for predicting heat ... (more)
Created Date 2015-05
Contributor Luna, Christopher Joseph (Author) / Tang, Wenbo (Thesis Director) / Treacy, Michael (Committee Member) / Orso, Meneghini (Committee Member) / Barrett, The Honors College / School of Mathematical and Statistical Sciences / Department of Physics
Subject Fusion Energy / Plasma Physics / Machine Learning
Series Academic Year 2014-2015
Type Text
Extent 16 pages
Language English
Reuse Permissions All Rights Reserved
Collaborating Institutions Barrett, the Honors College
Additional Formats MODS / OAI Dublin Core / RIS

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