Zobrazeno 1 - 10
of 1 295
pro vyhledávání: '"Churchill, R"'
Autor:
Gu, Junmin, Lin, Paul, Wu, Kesheng, Ku, Seung-Hoe, Chang, C. S., Churchill, R. Michael, Choi, Jong, Podhorszki, Norbert, Klasky, Scott
This work starts an in situ processing capability to study a certain diffusion process in magnetic confinement fusion. This diffusion process involves plasma particles that are likely to escape confinement. Such particles carry a significant amount o
Externí odkaz:
http://arxiv.org/abs/2311.01288
Autor:
Anirudh, Rushil, Archibald, Rick, Asif, M. Salman, Becker, Markus M., Benkadda, Sadruddin, Bremer, Peer-Timo, Budé, Rick H. S., Chang, C. S., Chen, Lei, Churchill, R. M., Citrin, Jonathan, Gaffney, Jim A, Gainaru, Ana, Gekelman, Walter, Gibbs, Tom, Hamaguchi, Satoshi, Hill, Christian, Humbird, Kelli, Jalas, Sören, Kawaguchi, Satoru, Kim, Gon-Ho, Kirchen, Manuel, Klasky, Scott, Kline, John L., Krushelnick, Karl, Kustowski, Bogdan, Lapenta, Giovanni, Li, Wenting, Ma, Tammy, Mason, Nigel J., Mesbah, Ali, Michoski, Craig, Munson, Todd, Murakami, Izumi, Najm, Habib N., Olofsson, K. Erik J., Park, Seolhye, Peterson, J. Luc, Probst, Michael, Pugmire, Dave, Sammuli, Brian, Sawlani, Kapil, Scheinker, Alexander, Schissel, David P., Shalloo, Rob J., Shinagawa, Jun, Seong, Jaegu, Spears, Brian K., Tennyson, Jonathan, Thiagarajan, Jayaraman, Ticoş, Catalin M., Trieschmann, Jan, van Dijk, Jan, Van Essen, Brian, Ventzek, Peter, Wang, Haimin, Wang, Jason T. L., Wang, Zhehui, Wende, Kristian, Xu, Xueqiao, Yamada, Hiroshi, Yokoyama, Tatsuya, Zhang, Xinhua
Publikováno v:
IEEE Transactions on Plasma Science 51, 1750 - 1838 (2023)
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine lear
Externí odkaz:
http://arxiv.org/abs/2205.15832
The simplified delta-f mixed-variable/pull-back electromagnetic simulation algorithm implemented in XGC for core plasma simulations by M. Cole et al. [Phys. Plasmas 28, 034501 (2021)] has been generalized to a total-f electromagnetic algorithm that c
Externí odkaz:
http://arxiv.org/abs/2202.06124
Particle-In-Cell (PIC) methods are frequently used for kinetic, high-fidelity simulations of plasmas. Implicit formulations of PIC algorithms feature strong conservation properties, up to numerical round-off errors, and are not subject to time-step l
Externí odkaz:
http://arxiv.org/abs/2110.12444
Autor:
Kube, Ralph, Churchill, R. Michael, Chang, CS, Choi, Jong, Wang, Jason, Klasky, Scott, Stephey, Laurie, Choi, Minjun, Dart, Eli
While experiments on fusion plasmas produce high-dimensional data time series with ever increasing magnitude and velocity, data analysis has been lagging behind this development. For example, many data analysis tasks are often performed in a manual,
Externí odkaz:
http://arxiv.org/abs/2108.08896
Autor:
Chang, C. S., Ku, S., Hager, R., Churchill, R. M., Hughes, J., Köchl, F., Loarte, A., Parail, V., Pitts, R.
Understanding and predicting divertor heat-load width ${\lambda}_q$ is a critically important problem for an easier and more robust operation of ITER with high fusion gain. Previous predictive simulation data for ${\lambda}_q$ using the extreme-scale
Externí odkaz:
http://arxiv.org/abs/2012.10750
An encoder-decoder neural network has been used to examine the possibility for acceleration of a partial integro-differential equation, the Fokker-Planck-Landau collision operator. This is part of the governing equation in the massively parallel part
Externí odkaz:
http://arxiv.org/abs/2009.06534
Autor:
Charidakos, I. Keramidas, Myra, J. R., Ku, S., Churchill, R. M., Hager, R., Chang, C. S., Parker, S.
The physical processes taking place at the edge region are crucial for the operation of tokamaks as they govern the interaction of hot plasma with the vessel walls. Numerical modeling of the edge with state-of-the-art codes attempts to elucidate inte
Externí odkaz:
http://arxiv.org/abs/2003.11214
Autor:
Churchill, R. M., team, the DIII-D
The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on sequences which
Externí odkaz:
http://arxiv.org/abs/1911.00149
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.