Feb. 15, 2024, 5:43 a.m. | Allen M. Wang, Oswin So, Charles Dawson, Darren T. Garnier, Cristina Rea, Chuchu Fan

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.09387v1 Announce Type: cross
Abstract: The tokamak offers a promising path to fusion energy, but plasma disruptions pose a major economic risk, motivating considerable advances in disruption avoidance. This work develops a reinforcement learning approach to this problem by training a policy to safely ramp-down the plasma current while avoiding limits on a number of quantities correlated with disruptions. The policy training environment is a hybrid physics and machine learning model trained on simulations of the SPARC primary reference discharge …

abstract advances arxiv cs.lg design differential disruption economic energy fusion major path physics.plasm-ph plasma policy ramp reinforcement reinforcement learning risk tokamak training trajectory type work

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