Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Bogdan Kustowski"'
Autor:
Matthew L Olson, Shusen Liu, Jayaraman J Thiagarajan, Bogdan Kustowski, Weng-Keen Wong, Rushil Anirudh
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 2, p 025054 (2024)
Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize be
Externí odkaz:
https://doaj.org/article/18fe84efb9f2405495678c53ed8890df
Autor:
Jim A. Gaffney, Kelli Humbird, Michael Kruse, Eugene Kur, Bogdan Kustowski, Ryan Nora, Brian Spears
Publikováno v:
Contributions to Plasma Physics.
Autor:
Michael J MacDonald, Carl Shneider, Patrick Knapp, Suzan Başeğmez du Pree, Derek Mariscal, B. Kettle, Will Trickey, Marta Fajardo, Suzanne Ali, Ben Williams, M. J. V. Streeter, Jonathan Citrin, J. J. Ruby, Gemma J. Anderson, Bogdan Kustowski, P. W. Hatfield, S. J. Rose, J. Luc Peterson, Jim Gaffney, Luca Antonelli, Madison E. Martin, Taisuke Nagayama, Charlotte Palmer
Publikováno v:
Nature, 593(7859), 351-361
Hatfield, P W, Gaffney, J A, Anderson, G J, Ali, S, Antonelli, L, Başeğmez du Pree, S, Citrin, J, Fajardo, M, Knapp, P, Kettle, B, Kustowski, B, MacDonald, M J, Mariscal, D, Martin, M E, Nagayama, T, Palmer, C A J, Peterson, J L, Rose, S, Ruby, J J, Shneider, C, Streeter, M J V, Trickey, W & Williams, B 2021, ' The data-driven future of high-energy-density physics ', Nature, vol. 593, pp. 351-361 . https://doi.org/10.1038/s41586-021-03382-w
Nature, 593, 351-361
Hatfield, P W, Gaffney, J A, Anderson, G J, Ali, S, Antonelli, L, Başeğmez du Pree, S, Citrin, J, Fajardo, M, Knapp, P, Kettle, B, Kustowski, B, MacDonald, M J, Mariscal, D, Martin, M E, Nagayama, T, Palmer, C A J, Peterson, J L, Rose, S, Ruby, J J, Shneider, C, Streeter, M J V, Trickey, W & Williams, B 2021, ' The data-driven future of high-energy-density physics ', Nature, vol. 593, pp. 351-361 . https://doi.org/10.1038/s41586-021-03382-w
Nature, 593, 351-361
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
Transfer Learning as a Tool for Reducing Simulation Bias: Application to Inertial Confinement Fusion
Autor:
Bogdan Kustowski, Brian Spears, Rushil Anirudh, Gemma J. Anderson, Jayaraman J. Thiagarajan, Jim Gaffney
Publikováno v:
IEEE Transactions on Plasma Science. 48:46-53
We adopt a technique, known in the machine learning community as transfer learning, to reduce the bias of computer simulation using very sparse experimental data. Unlike the Bayesian calibration, which is commonly used to estimate the simulation bias
Autor:
Bogdan Kustowski, Jim A Gaffney, Brian K Spears, Gemma J Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J Thiagarajan, Michael K G Kruse, Ryan C Nora
Publikováno v:
Machine Learning: Science and Technology. 3:015035
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.
Autor:
J. Luc Peterson, Ben Bay, Joe Koning, Peter Robinson, Jessica Semler, Jeremy White, Rushil Anirudh, Kevin Athey, Peer-Timo Bremer, Francesco Di Natale, David Fox, Jim A. Gaffney, Sam A. Jacobs, Bhavya Kailkhura, Bogdan Kustowski, Steven Langer, Brian Spears, Jayaraman Thiagarajan, Brian Van Essen, Jae-Seung Yeom
With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f30692598ec549d3371d487aaad810a0
Autor:
Bogdan Kustowski, Jayaraman J. Thiagarajan, Brian Spears, Jim Gaffney, Rushil Anirudh, Gemma J. Anderson
Publikováno v:
IEEE Transactions on Plasma Science. 48:3275-3275
Publikováno v:
Geophysical Journal International. 174:978-992
SUMMARY We develop an approach that allows us to invert for the mantle velocity structure within a finely parametrized region as a perturbation with respect to a low-resolution, global tomographic model. We implement this technique to investigate the
Publikováno v:
Earth and Planetary Science Letters. 267:213-227
Seismic shear waves that are polarized horizontally (SH) generally travel faster in the upper mantle than those that are polarized vertically (SV), and deformation of rocks under dislocation creep has been invoked to explain such radial anisotropy. C
Publikováno v:
Bulletin of the Seismological Society of America. 97:1756-1762
We present an efficient and accurate method for calculating local nonlinear crustal effects on the eigenfrequency of free oscillations of the Earth. We implement the method in the calculation of synthetic normal-mode seismograms and demonstrate that