Viktor Reshniak

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Junqi YinNational Center for Computational Sciences, Oak Ridge National LaboratoryVerified email at ornl.gov
A Q M KhaliqProfessor of Mathematics, Middle Tennessee State UniversityVerified email at mtsu.edu
Guannan ZhangOak Ridge National LaboratoryVerified email at ornl.gov
Massimiliano Lupo PasiniOak Ridge National LaboratoryVerified email at ornl.gov
Clayton WebsterUniversity of Texas at AustinVerified email at utexas.edu
Dmytro Yevdokymov (Дмитрий Васил...Дніпровський національний університет імені Олеся Гончара (Oles Honchar Dnipro National University)Verified email at mmf.dnulive.dp.ua
Юлия Владимировна Бразалук// Iulii...Дніпровський національний університет імені Олеся ГончараVerified email at dnu.dp.ua
Qian GongOak Ridge National Lab, Fermilab, Duke UniversityVerified email at ornl.gov
Jieyang ChenUniversity of Alabama at Birmingham, Oak Ridge National Laboratory, University of CaliforniaVerified email at ornl.gov
Lipeng WanGeorgia State UniversityVerified email at gsu.edu
Anand RangarajanProfessor, Dept. of CISE, Univ. of FloridaVerified email at cise.ufl.edu
Norbert PodhorszkiScientific Data Group, Oak Ridge National LaboratoryVerified email at ornl.gov
Xin LiangUniversity of KentuckyVerified email at uky.edu
Sanjay RankaUniversity of FloridaVerified email at cise.ufl.edu
Scott KlaskyOak Ridge National LaboratoryVerified email at ornl.gov
Harold A. Lay, Jr.Thompson Machinery Commerce CorporationVerified email at tmcat.com
Tania BanerjeeUniversity of FloridaVerified email at ufl.edu
Rick ArchibaldORNLVerified email at ornl.gov
Ana GainaruOak Ridge National LaboratoryVerified email at ornl.gov
Jaemoon LeeUniversity of FloridaVerified email at ufl.edu

Viktor Reshniak

Oak Ridge National Laboratory

Verified email at ornl.gov - Homepage

Numerical analysis Scientific computing Machine learning Data compression


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Split-step Milstein methods for multi-channel stiff stochastic differential systems V Reshniak, AQM Khaliq, DA Voss, G Zhang Applied Numerical Mathematics 89, 1-23, 2015	32	2015
Robust learning with implicit residual networks V Reshniak, CG Webster Machine Learning and Knowledge Extraction 3 (1), 34-55, 2020	13	2020
On the implementation of multilevel Monte Carlo simulation of the stochastic volatility and interest rate model using multi-GPU clusters HA Lay, Z Colgin, V Reshniak, AQM Khaliq Monte Carlo Methods and Applications 24 (4), 309-321, 2018	5	2018
A semi-analytical approach to Green׳ s functions for heat equation in regions of irregular shape YA Melnikov, V Reshniak Engineering Analysis with Boundary Elements 46, 108-115, 2014	5	2014
Anderson acceleration for distributed training of deep learning models ML Pasini, J Yin, V Reshniak, MK Stoyanov SoutheastCon 2022, 289-295, 2022	4	2022
Stable Anderson acceleration for deep learning ML Pasini, J Yin, V Reshniak, M Stoyanov arXiv preprint arXiv:2110.14813, 2021	4	2021
Some further developments in the infinite product representation of elementary functions V Reshniak Global Journal of Science Frontier Research 13 (4), 2013	3	2013
Расчет обтекания сложных гидродинамических конфигураций комбинированным методом граничных элементов и дискретных вихрей ЮВ Бразалук, ДВ Евдокимов, ВГ Решняк Вісник Дніпропетровського університету. Серія: Механіка, 50-67, 2012	3	2012
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring Q Gong, J Chen, B Whitney, X Liang, V Reshniak, T Banerjee, J Lee, ... SoftwareX 24, 101590, 2023	2	2023
A nonlocal feature-driven exemplar-based approach for image inpainting V Reshniak, J Trageser, CG Webster SIAM Journal on Imaging Sciences 13 (4), 2140-2168, 2020	2	2020
Slow-scale split-step tau-leap method for stiff stochastic chemical systems V Reshniak, A Khaliq, D Voss Journal of Computational and Applied Mathematics 361, 79-96, 2019	2	2019
Spatiotemporally adaptive compression for scientific dataset with feature preservation–a case study on simulation data with extreme climate events analysis Q Gong, C Zhang, X Liang, V Reshniak, J Chen, A Rangarajan, S Ranka, ... 2023 IEEE 19th International Conference on e-Science (e-Science), 1-10, 2023	1	2023
Reducing computational cost of the multilevel Monte Carlo method by construction of suitable pathwise integrators V Reshniak Middle Tennessee State University, 2017	1	2017
A Review of Sensing Technologies for New, Low Global Warming Potential (GWP), Flammable Refrigerants V Reshniak, P Cheekatamarla, V Sharma, S Yana Motta Energies 16 (18), 6499, 2023		2023
A scalable transformer model for real-time decision making in neutron scattering experiments J Yin, S Liu, V Reshniak, X Wang, G Zhang Journal of Machine Learning for Modeling and Computing 4 (1), 2023		2023
Dissipative residual layers for unsupervised implicit parameterization of data manifolds V Reshniak arXiv preprint arXiv:2210.07100, 2022		2022
Anderson Acceleration for Distributed Training of Deep Learning Models M Lupo Pasini, J Yin, V Reshniak, M Stoyanov Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), 2022		2022
Stable Anderson Acceleration for Deep Learning M Lupo Pasini, J Yin, V Reshniak, M Stoyanov arXiv e-prints, arXiv: 2110.14813, 2021		2021
AI-Based Approach for Advancing the Understanding of Spatiotemporal Drought Characteristics D Rastogi, K Kurte, V Reshniak, O Kotevska Artificial Intelligence for Earth System Predictability (AI4ESP …, 2021		2021
Method of Green's potentials for elliptic PDEs in domains with random apertures V Reshniak, Y Melnikov Journal of Scientific Computing 84 (3), 2020		2020

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