Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Elise Jennings"'
Publikováno v:
Physics Letters B, Vol 795, Iss , Pp 248-258 (2019)
The scale of ongoing and future electromagnetic surveys pose formidable challenges to classify astronomical objects. Pioneering efforts on this front include citizen science campaigns adopted by the Sloan Digital Sky Survey (SDSS). SDSS datasets have
Externí odkaz:
https://doaj.org/article/20fa6f76ec684b5bbd1c3ec023bbd375
Publikováno v:
Nuclear Science and Engineering. 195:520-537
The System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non–lig...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a97c8cfac99a0192a67f7104d30e35d2
https://doi.org/10.1080/00295639.2020.1840238
https://doi.org/10.1080/00295639.2020.1840238
Publikováno v:
Physics Letters B, Vol 795, Iss, Pp 248-258 (2019)
Physics Letters
Physics Letters
The scale of ongoing and future electromagnetic surveys pose formidable challenges to classify astronomical objects. Pioneering efforts on this front include citizen science campaigns adopted by the Sloan Digital Sky Survey (SDSS). SDSS datasets have
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e58cbf9ef32b45dd5dd46305d9dcbce2
http://www.sciencedirect.com/science/article/pii/S0370269319303879
http://www.sciencedirect.com/science/article/pii/S0370269319303879
Publikováno v:
AIAA Scitech 2021 Forum.
We outline the development of a data science module within OpenFOAM which allows for the in-situ deployment of trained deep learning architectures for general-purpose predictive tasks. This module is constructed with the TensorFlow C API and is integ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ebf9d696af6aa8b641fd9080dab9092b
https://doi.org/10.2514/6.2021-1485
https://doi.org/10.2514/6.2021-1485
Autor:
Elise Jennings, Himanshu Sharma
Bayesian neural Networks (BNNs) are a promising method of obtaining statistical uncertainties for neural network predictions but with a higher computational overhead which can limit their practical usage. This work explores the use of high performanc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef637728946eb61648b9976961429833
http://arxiv.org/abs/2005.11619
http://arxiv.org/abs/2005.11619
Publikováno v:
Computers & Fluids. 227:104777
The Reynolds-averaged Navier-Stokes (RANS) equations for steady-state assessment of incompressible turbulent flows remain the workhorse for practical computational fluid dynamics (CFD) applications. Consequently, improvements in speed or accuracy hav
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0803eb4d3dbd633607229d1e90fcd1d9
https://doi.org/10.1016/j.compfluid.2020.104777
https://doi.org/10.1016/j.compfluid.2020.104777
Publikováno v:
PMBS@SC
Shared network topologies, such as dragonfly, subject applications to unavoidable inter-job interference arising from congestion on shared network links. Quantifying the impact of congestion is essential for effectively assessing and comparing the ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c1da3760c9586300d2dd2abe3038510
https://doi.org/10.1109/pmbs49563.2019.00013
https://doi.org/10.1109/pmbs49563.2019.00013
Autor:
George Rojas, Elise Jennings, Vitali Morozov, Eric Pershey, Liza Booker, Kathy DiBennardi, Anouar Benali, Marta Garcia, Wei Jiang, Adam Scovel, Bill Allcock, Sreeranjani Ramprakash, Carissa Holohan, Yuri Alexeev, Ray Loy, Laura Wolf, Sudheer Chunduri, Nichols A. Romero, Janet Knowles, Zachary Nault, Lisa Childers, Richard Coffey, Christopher Knight, Derek Jensen, Michael E. Papka, Gabe West, Margaret Kaczmarski, Michael Zhang, Ben Lenard, Saumil Patel, Benjamin S. Allen, Venkatram Vishwanath, Beth Cerny, Timothy J. Williams, Álvaro Vázquez-Mayagoitia, Joseph A. Insley, Avanthi Mantrala, Preeti Malakar, Anping Liu, Graham D. Fletcher, Robert Scott, David E. Martin, Guillermo Mayorga, Joan Stover, Brian Toonen, Denise Nelson, Adrian Pope, Darin Wills, Ira Goldberg, Katherine Riley, Pragnesh Patel, Edouard Brooks, Renee Plzak, Cheetah Goletz, R. Douglas Waldron, James C. Osborn, Kalyan Kumaran, Silvio Rizzi, Hal Finkel, Paul Messina, Janet Jaseckas, Emily Shemon, Ramesh Balakrishnan, Jack O’Connell, Susan Gregurich, William Scullin, Mark R. Fahey, Scott Parker, Ti Leggett, Phil Carns, Ashley Boyle, Ginny Doyle, Ryan Milner, Tommie Jackson, Paul Coffman, Haritha Siddabathuni Som, Susan Coghlan, Kevin Harms, Jared Punzel, John ‘Skip’ Reddy, Andrew Cherry, Paul Rich, Tom Uram, James Collins, Mirek Suliba, Gordon McPheeters, Laural Briggs, Wanda Woods
Publikováno v:
Contemporary High Performance Computing ISBN: 9781351036863
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6defe81860f43fbd75d4dfa6efdac312
https://doi.org/10.1201/9781351036863-2
https://doi.org/10.1201/9781351036863-2
Autor:
Narayanan Kasthuri, Hanyu Li, Samuel Flender, Wushi Dong, Rafael Vescovi, Thomas D. Uram, Venkatram Vishwanath, Peter B. Littlewood, Nicola J. Ferrier, Elise Jennings, Corey Adams, Murat Keçeli
Publikováno v:
DLS@SC
Mapping all the neurons in the brain requires automatic reconstruction of entire cells from volume electron microscopy data. The flood-filling network (FFN) architecture has demonstrated leading performance for segmenting structures from this data. H
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0bc7d4cd5a032f89caf7c32915b5e39b
Publikováno v:
Monthly Notices of the Royal Astronomical Society. 457:1076-1088
We present the nonlinear 2D galaxy power spectrum, $P(k,\mu)$, in redshift space, measured from the Dark Sky simulations, using galaxy catalogs constructed with both halo occupation distribution and subhalo abundance matching methods, chosen to repre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0aee69a29a7bd9f7eaaaf8a542f7aed
https://doi.org/10.1093/mnras/stv2989
https://doi.org/10.1093/mnras/stv2989