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pro vyhledávání: '"Rea, Cristina"'
Autor:
Chayapathy, Dhruva, Siebert, Tavis, Spangher, Lucas, Moharir, Akshata Kishore, Patil, Om Manoj, Rea, Cristina
Machine Learning guided data augmentation may support the development of technologies in the physical sciences, such as nuclear fusion tokamaks. Here we endeavor to study the problem of detecting disruptions i.e. plasma instabilities that can cause s
Externí odkaz:
http://arxiv.org/abs/2410.11065
Autor:
Maris, Andrew, Rea, Cristina, Pau, Alessandro, Hu, Wenhui, Xiao, Bingjia, Granetz, Robert, Marmar, Earl, team, the EUROfusion Tokamak Exploitation, team, the Alcator C-Mod, team, the ASDEX Upgrade, team, the DIII-D, team, the EAST, team, the TCV
The "density limit" is one of the fundamental bounds on tokamak operating space, and is commonly estimated via the empirical Greenwald scaling. This limit has garnered renewed interest in recent years as it has become clear that ITER and many tokamak
Externí odkaz:
http://arxiv.org/abs/2406.18442
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 poli
Externí odkaz:
http://arxiv.org/abs/2402.09387
The physical sciences require models tailored to specific nuances of different dynamics. In this work, we study outcome predictions in nuclear fusion tokamaks, where a major challenge are \textit{disruptions}, or the loss of plasma stability with dam
Externí odkaz:
http://arxiv.org/abs/2401.00051
While fusion reactors known as tokamaks hold promise as a firm energy source, advances in plasma control, and handling of events where control of plasmas is lost, are needed for them to be economical. A significant bottleneck towards applying more ad
Externí odkaz:
http://arxiv.org/abs/2310.20079
Autor:
Tang, William, Dong, Ge, Barr, Jayson, Erickson, Keith, Conlin, Rory, Boyer, M. Dan, Kates-Harbeck, Julian, Felker, Kyle, Rea, Cristina, Logan, Nikolas C., Svyatkovskiy, Alexey, Feibush, Eliot, Abbatte, Joseph, Clement, Mitchell, Grierson, Brian, Nazikian, Raffi, Lin, Zhihong, Eldon, David, Moser, Auna, Maslov, Mikhail
This paper reports on advances to the state-of-the-art deep-learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced a 2019 Nature publication. In particular, the predictor now features not only
Externí odkaz:
http://arxiv.org/abs/2204.01289
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Publikováno v:
Fusion Science and Technology; July 2024, Vol. 80 Issue: 5 p636-652, 17p
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