Towards practical reinforcement learning for tokamak magnetic control-Reference-Cited by-同舟云学术

Towards practical reinforcement learning for tokamak magnetic control

Published:2024-03 Issue: Volume:200 Page:114161
ISSN:0920-3796
Container-title:Fusion Engineering and Design
language:en
Short-container-title:Fusion Engineering and Design

Author:

Tracey Brendan D.^ORCID,Michi Andrea^ORCID,Chervonyi Yuri^ORCID,Davies Ian,Paduraru Cosmin^ORCID,Lazic Nevena,Felici Federico^ORCID,Ewalds Timo^ORCID,Donner Craig^ORCID,Galperti Cristian,Buchli Jonas,Neunert Michael,Huber Andrea,Evens Jonathan^ORCID,Kurylowicz Paula,Mankowitz Daniel J.,Riedmiller Martin^ORCID

Funder

Euratom Research and Training Programme

EUROfusion Consortium Research Institutions

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Swiss State Secretariat for Education Research and Innovation

Publisher

Elsevier BV

Reference54 articles.

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2. Disruptions in ITER and strategies for their control and mitigation;Lehnen;J. Nucl. Mater.,2015

3. Mitigation of divertor heat loads by strike point sweeping in high power JET discharges;Silburn;Phys. Scr.,2017

4. Divertor heat flux control research on DIII-D;Leonard;Fusion Sci. Technol.,2005

5. Influence of plasma shape on transport in the TCV tokamak;Moret;Phys. Rev. Lett.,1997

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2. Avoiding fusion plasma tearing instability with deep reinforcement learning;Nature;2024-02-21