Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Viktor Reshniak"'
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
Autor:
Qian Gong, Jieyang Chen, Ben Whitney, Xin Liang, Viktor Reshniak, Tania Banerjee, Jaemoon Lee, Anand Rangarajan, Lipeng Wan, Nicolas Vidal, Qing Liu, Ana Gainaru, Norbert Podhorszki, Richard Archibald, Sanjay Ranka, Scott Klasky
Publikováno v:
SoftwareX, Vol 24, Iss , Pp 101590- (2023)
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capability and precise error control, MGARD addresses a wide range of requ
Externí odkaz:
https://doaj.org/article/e7ccffb856204f6982e53466473b0e3c
A Review of Sensing Technologies for New, Low Global Warming Potential (GWP), Flammable Refrigerants
Publikováno v:
Energies, Vol 16, Iss 18, p 6499 (2023)
Commercial refrigeration systems currently utilize refrigerants with global warming potential (GWP) values ranging from 1250 to 4000. The advent of low GWP alternatives (GWP <150) is expected to significantly curtail direct emissions from this segmen
Externí odkaz:
https://doaj.org/article/9050cb1ed95e4c16b92c41a48ae9836c
Autor:
Viktor Reshniak, Clayton G. Webster
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 3, Iss 1, Pp 34-55 (2020)
In this effort, we propose a new deep architecture utilizing residual blocks inspired by implicit discretization schemes. As opposed to the standard feed-forward networks, the outputs of the proposed implicit residual blocks are defined as the fixed
Externí odkaz:
https://doaj.org/article/f83bb295705c4e0887c85402ff62f033
Publikováno v:
Journal of Machine Learning for Modeling and Computing.
Publikováno v:
SoutheastCon 2022.
Publikováno v:
SIAM Journal on Imaging Sciences. 13:2140-2168
We present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework. The recovery of geometric structures is achieved by using general convolution operators as a
Publikováno v:
Monte Carlo Methods and Applications. 24:309-321
We explore different methods of solving systems of stochastic differential equations by first implementing the Euler–Maruyama and Milstein methods with a Monte Carlo simulation on a CPU. The performance of the methods is significantly improved thro
Autor:
Clayton G. Webster, Viktor Reshniak
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 3, Iss 3, Pp 34-55 (2021)
Machine Learning and Knowledge Extraction
Volume 3
Issue 1
Pages 3-55
Machine Learning and Knowledge Extraction
Volume 3
Issue 1
Pages 3-55
In this effort, we propose a new deep architecture utilizing residual blocks inspired by implicit discretization schemes. As opposed to the standard feed-forward networks, the outputs of the proposed implicit residual blocks are defined as the fixed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1856281978f7cb7c20384100b117a70
Autor:
Yuri A. Melnikov, Viktor Reshniak
Problems with topological uncertainties appear in many fields ranging from nano-device engineering to the design of bridges. In many of such problems, a part of the domains boundaries is subjected to random perturbations making inefficient convention
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3857575c4815698ca3c63e83a42a9bd
http://arxiv.org/abs/1812.07140
http://arxiv.org/abs/1812.07140