Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Citrin, Jonathan"'
Large scale validation and uncertainty quantification are essential in the experimental design, control, and operations of fusion reactors. Reduced models and increasing computational power means that it is possible to run many simulations, yet setti
Externí odkaz:
http://arxiv.org/abs/2409.13529
Autor:
Citrin, Jonathan, Goodfellow, Ian, Raju, Akhil, Chen, Jeremy, Degrave, Jonas, Donner, Craig, Felici, Federico, Hamel, Philippe, Huber, Andrea, Nikulin, Dmitry, Pfau, David, Tracey, Brendan, Riedmiller, Martin, Kohli, Pushmeet
We present TORAX, a new, open-source, differentiable tokamak core transport simulator implemented in Python using the JAX framework. TORAX solves the coupled equations for ion heat transport, electron heat transport, particle transport, and current d
Externí odkaz:
http://arxiv.org/abs/2406.06718
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
A novel combination of two widely-used clustering algorithms is proposed here for the detection and reduction of high data density regions. The Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used for the detection o
Externí odkaz:
http://arxiv.org/abs/2111.12559
Autor:
Hatfield, Peter W., Gaffney, Jim A., Anderson, Gemma J., Ali, Suzanne, Antonelli, Luca, Pree, Suzan Başeğmez du, Citrin, Jonathan, Fajardo, Marta, Knapp, Patrick, Kettle, Brendan, Kustowski, Bogdan, MacDonald, Michael J., Mariscal, Derek, Martin, Madison E., Nagayama, Taisuke, Palmer, Charlotte A. J., Peterson, J. Luc, Rose, Steven, Ruby, J J, Shneider, Carl, Streeter, Matt J. V., Trickey, Will, Williams, Ben
Publikováno v:
Nature, 593, 7859, 351-361, 2021
The study of plasma physics under conditions of extreme temperatures, densities and electromagnetic field strengths is significant for our understanding of astrophysics, nuclear fusion and fundamental physics. These extreme physical systems are stron
Externí odkaz:
http://arxiv.org/abs/2111.11310
Autor:
Ho, Aaron, Citrin, Jonathan, Auriemma, Fulvio, Bourdelle, Clarisse, Casson, Francis J., Kim, Hyun-Tae, Manas, Pierre, Szepesi, Gabor, Weisen, Henri, Contributors, JET
Publikováno v:
Nucl. Fusion 59 056007 (2019)
This paper outlines an approach towards improved rigour in tokamak turbulence transport model validation within integrated modelling. Gaussian process regression (GPR) techniques were applied for profile fitting during the preparation of integrated m
Externí odkaz:
http://arxiv.org/abs/2105.01177
Autor:
Ho, Aaron, Citrin, Jonathan, Bourdelle, Clarisse, Camenen, Yann, Casson, Francis J., van de Plassche, Karel L., Weisen, Henri, Contributors, JET
Publikováno v:
Physics of Plasmas 28, 032305 (2021)
Within integrated tokamak plasma modelling, turbulent transport codes are typically the computational bottleneck limiting their routine use outside of post-discharge analysis. Neural network (NN) surrogates have been used to accelerate these calculat
Externí odkaz:
http://arxiv.org/abs/2105.01168
Autor:
Stephens, Cole Darin, Garbet, Xavier, Citrin, Jonathan, Bourdelle, Clarisse, van de Plassche, Karel Lucas, Jenko, Frank
Publikováno v:
Journal of Plasma Physics, Volume 87, Issue 4, August 2021, 905870409
In order to predict and analyze turbulent transport in tokamaks, it is important to model transport that arises from microinstabilities. For this task, quasilinear codes have been developed that seek to calculate particle, angular momentum, and heat
Externí odkaz:
http://arxiv.org/abs/2103.10569
Autor:
van de Plassche, Karel Lucas, Citrin, Jonathan, Bourdelle, Clarisse, Camenen, Yann, Casson, Francis J., Dagnelie, Victor I., Felici, Federico, Ho, Aaron, Van Mulders, Simon, Contributors, JET
We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model Q
Externí odkaz:
http://arxiv.org/abs/1911.05617
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