Velimir V Vesselinov ("monty")

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Daniel O'MalleyLos Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Maruti Kumar MudunuruPacific Northwest National LaboratoryPatvirtintas el. paštas pnnl.gov
Bulbul AhmmedLos Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Boian S. AlexandrovTheoretical Division, Los Alamos National LabPatvirtintas el. paštas lanl.gov
Dylan HarpThe Freshwater TrustPatvirtintas el. paštas thefreshwatertrust.org
Walter A. IllmanProfessor of Hydrogeology, University of WaterlooPatvirtintas el. paštas uwaterloo.ca
Richard S. MiddletonCarbon Solutions LLCPatvirtintas el. paštas carbonsolutionsllc.com
Valentin StanevUniversity of Maryland, College ParkPatvirtintas el. paštas umd.edu
Elizabeth Harrison KeatingSenior Scientist, Los Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Kay BirdsellLos Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Jasper A. VrugtEarth System Science & EngineeringPatvirtintas el. paštas uci.edu
Zhiming LuStaff Scientist, Los Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Scott K. HansenBen-Gurion University of the NegevPatvirtintas el. paštas bgu.ac.il
Youzuo LinAssociate Professor at University of North Carolina at Chapel HillPatvirtintas el. paštas unc.edu
Phoolendra MishraProfessor, California State University FullertonPatvirtintas el. paštas fullerton.edu
Daniel M. TartakovskyStanford UniversityPatvirtintas el. paštas stanford.edu
Philip H. StaufferSenior Hydrogeologist, Los Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Ludmil B. AlexandrovProfessor, University of California San DiegoPatvirtintas el. paštas ucsd.edu
David BroxtonLos Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Jeffrey M HeikoopDeputy Group Leader, Earth Systems Observations, Los Alamos National LaboratoryPatvirtintas el. paštas lanl.gov

Stebėti

Velimir V Vesselinov ("monty")

SmartTensors LLC, Santa Fe, New Mexico, USA

Patvirtintas el. paštas smarttensors.com - Pagrindinis puslapis

Machine Learning Model analyses Uncertainty quantification Decision analysis


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Inverse modeling of subsurface flow and transport properties: A review with new developments JA Vrugt, PH Stauffer, T Wohling, BA Robinson, VV Vesselinov Vadose zone journal 7 (2), 843-864, 2008	265	2008
Integrated modeling approach for optimal management of water, energy and food security nexus X Zhang, VV Vesselinov Advances in Water Resources 101, 1-10, 2017	261	2017
Three-dimensional numerical inversion of pneumatic cross-hole tests in unsaturated fractured tuff: 2. Equivalent parameters, high-resolution stochastic imaging and scale effects VV Vesselinov, SP Neuman, WA Illman Water Resources Research 37 (12), 3019-3041, 2001	173	2001
Energy-water nexus: Balancing the tradeoffs between two-level decision makers X Zhang, VV Vesselinov Applied energy 183, 77-87, 2016	170	2016
Nonnegative/binary matrix factorization with a d-wave quantum annealer D O’Malley, VV Vesselinov, BS Alexandrov, LB Alexandrov PloS one 13 (12), e0206653, 2018	144	2018
Three-dimensional numerical inversion of pneumatic cross-hole tests in unsaturated fractured tuff 1. Methodology and borehole effects VV Vesselinov, SP Neuman, WA Illman Water resources research 37 (12), 3001-3017, 2001	135	2001
Conceptual models of flow and transport in the fractured vadose zone National Research Council, Commission on Geosciences, ... National Academies Press, 2001	118	2001
Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering V Stanev, VV Vesselinov, AG Kusne, G Antoszewski, I Takeuchi, ... npj Computational Materials 4 (1), 43, 2018	95	2018
Multifidelity Monte Carlo estimation of variance and sensitivity indices E Qian, B Peherstorfer, D O'Malley, VV Vesselinov, K Willcox SIAM/ASA Journal on Uncertainty Quantification 6 (2), 683-706, 2018	88	2018
Active layer hydrology in an arctic tundra ecosystem: quantifying water sources and cycling using water stable isotopes HM Throckmorton, BD Newman, JM Heikoop, GB Perkins, X Feng, ... Hydrological Processes 30 (26), 4972-4986, 2016	87	2016
Contaminant source identification using semi-supervised machine learning VV Vesselinov, BS Alexandrov, D O’Malley Journal of contaminant hydrology 212, 134-142, 2018	70	2018
Theoretical interpretation of a pronounced permeability scale effect in unsaturated fractured tuff Y Hyun, SP Neuman, VV Vesselinov, WA Illman, DM Tartakovsky, ... Water Resources Research 38 (6), 1092, 2002	69	2002
Aquifer structure identification using stochastic inversion DR Harp, Z Dai, AV Wolfsberg, JA Vrugt, BA Robinson, VV Vesselinov Geophysical Research Letters 35 (8), L08404, 2008	65	2008
Blind source separation for groundwater pressure analysis based on nonnegative matrix factorization BS Alexandrov, VV Vesselinov Water Resources Research 50 (9), 7332-7347, 2014	62	2014
Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing VV Vesselinov, MK Mudunuru, S Karra, D O'Malley, BS Alexandrov Journal of Computational Physics 395, 85-104, 2019	49	2019
Toq. jl: A high-level programming language for d-wave machines based on julia D O'Malley, VV Vesselinov 2016 IEEE High Performance Extreme Computing Conference (HPEC), 1-7, 2016	47	2016
Geostatistical, type curve and inverse analyses of pneumatic injection tests in unsaturated fractured tuffs at the Apache Leap Research Site near Superior, Arizona G Chen, WA Illman, DL Thompson, VV Vesselinov, SP Neuman Dynamics of Fluids in Fractured Rocks, Geophys. Monogr. Ser 122, 73-98, 2000	45	2000
Single-and cross-hole pneumatic tests in unsaturated fractured tuffs at the Apache Leap Research Site: Phenomenology, spatial variability, connectivity and scale WA Illman US Nuclear Regulatory Commission, 1998	45	1998
Interdisciplinary studies on the technical and economic feasibility of deep underground coal gasification with CO2 storage in bulgaria Y Sheng, A Benderev, D Bukolska, KII Eshiet, CD Da Gama, T Gorka, ... Mitigation and Adaptation Strategies for Global Change 21 (4), 595-627, 2016	41	2016
Machine-learning predictions of the shale wells’ performance M Mehana, E Guiltinan, V Vesselinov, R Middleton, JD Hyman, Q Kang, ... Journal of Natural Gas Science and Engineering 88, 103819, 2021	39	2021

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