Hannover 2020 – wissenschaftliches Programm
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P: Fachverband Plasmaphysik
P 16: Poster Session 3
P 16.4: Poster
Mittwoch, 11. März 2020, 16:30–18:30, Empore Lichthof
Machine Learning Methods as Surrogate Models for the Power Exhaust in Tokamaks — •Martin Brenzke1, Sven Wiesen1, Matthias Bernert2, David Coster2, Jenia Jitsev3, Udo von Toussaint2, EUROfusion MST1 Team4, and the ASDEX Upgrade Team5 — 1Forschungszentrum Jülich, Institut für Energie- und Klimaforschung, 52425 Jülich, Germany — 2Max Planck Institute for Plasma Physics, 85748 Garching, Germany — 3Jülich Supercomputing Center (JSC), Institute for Advanced Simulation (IAS), Research Center Jülich, 52425 Jülich, Germany — 4See the author list of B. Labit et al. 2019 Nucl. Fusion 59 086020 — 5See the author list of H. Meyer et al. 2019 Nucl. Fusion 59 112014
One of the main challenges in the design of an economically viable fusion reactor are the thermal loads experienced by the targets in a divertor-based design. These thermal loads cause degradation of the target material and limit the lifetime of a divertor. Modeling these thermal loads is one of the most important points in determining the operating scenarios for future fusion devices and remains a challeng- ing yet crucial task. In light of current developments and successes in the field of machine learning techniques, data-driven modeling is an interesting option for this problem. We present results for a ma- chine learning-based modeling approach using experimental data from the ASDEX Upgrade experiment. We focus on a comparison of the performances of several machine learning approaches.