Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Spears, Brian K."'
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
Autor:
Shukla, Ankita, Anirudh, Rushil, Kur, Eugene, Thiagarajan, Jayaraman J., Bremer, Peer-Timo, Spears, Brian K., Ma, Tammy, Turaga, Pavan
In this paper, we develop a Wasserstein autoencoder (WAE) with a hyperspherical prior for multimodal data in the application of inertial confinement fusion. Unlike a typical hyperspherical generative model that requires computationally inefficient sa
Externí odkaz:
http://arxiv.org/abs/2111.12798
Autor:
Kustowski, Bogdan, Gaffney, Jim A., Spears, Brian K., Anderson, Gemma J., Anirudh, Rushil, Bremer, Peer-Timo, Thiagarajan, Jayaraman J., Kruse, Michael K. G., Nora, Ryan C.
Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional.
Externí odkaz:
http://arxiv.org/abs/2104.09684
Autor:
Anderson, Gemma J., Gaffney, Jim A., Spears, Brian K., Bremer, Peer-Timo, Anirudh, Rushil, Thiagarajan, Jayaraman J.
Large-scale numerical simulations are used across many scientific disciplines to facilitate experimental development and provide insights into underlying physical processes, but they come with a significant computational cost. Deep neural networks (D
Externí odkaz:
http://arxiv.org/abs/2010.13749
Neural networks have become very popular in surrogate modeling because of their ability to characterize arbitrary, high dimensional functions in a data driven fashion. This paper advocates for the training of surrogates that are consistent with the p
Externí odkaz:
http://arxiv.org/abs/1912.08113
There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-driven fashion. However, a common challenge is to verify the scientific pla
Externí odkaz:
http://arxiv.org/abs/1910.01666
Autor:
Liu, Shusen, Wang, Di, Maljovec, Dan, Anirudh, Rushil, Thiagarajan, Jayaraman J., Jacobs, Sam Ade, Van Essen, Brian C., Hysom, David, Yeom, Jae-Seung, Gaffney, Jim, Peterson, Luc, Robinson, Peter B., Bhatia, Harsh, Pascucci, Valerio, Spears, Brian K., Bremer, Peer-Timo
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) cal
Externí odkaz:
http://arxiv.org/abs/1907.08325
Autor:
Spears, Brian K.
Machine learning is finding increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inp
Externí odkaz:
http://arxiv.org/abs/1712.08523
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2020 May 01. 117(18), 9741-9746.
Externí odkaz:
https://www.jstor.org/stable/26930009
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