Daniel O'Malley

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Hari ViswanathanSenior Scientist, Los Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Velimir V Vesselinov ("monty")SmartTensors LLC, Santa Fe, New Mexico, USAPatvirtintas el. paštas smarttensors.com
Jeffrey HymanEES- 16, Los Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
john cushmanpurduePatvirtintas el. paštas purdue.edu
John GoldenLos Alamos National LabPatvirtintas el. paštas lanl.gov
Boian S. AlexandrovTheoretical Division, Los Alamos National LabPatvirtintas el. paštas lanl.gov
Teeratorn KadeethumSandia National LaboratoriesPatvirtintas el. paštas sandia.gov
Shriram SrinivasanApplied Mathematics and Plasma Physics Group, Theoretical Division, Los Alamos NationalPatvirtintas el. paštas lanl.gov
Maruti Kumar MudunuruPacific Northwest National LaboratoryPatvirtintas el. paštas pnnl.gov
Hongkyu Yoonsandia national laboratoriesPatvirtintas el. paštas sandia.gov
Stephan EidenbenzComputer Science, Los Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Youngsoo ChoiResearch Scientist, LLNLPatvirtintas el. paštas llnl.gov
Luke FrashLos Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Moongyu ParkPurdue UniversityPatvirtintas el. paštas purdue.edu
Andreas BärtschiScientist, Los Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Nikolaos BouklasAssociate Professor, Cornell UniversityPatvirtintas el. paštas cornell.edu
Nataliia MakedonskaScientist at Los Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Abigail HunterLos Alamos National LaboratoryPatvirtintas el. paštas lanl.gov
Dylan HarpThe Freshwater TrustPatvirtintas el. paštas thefreshwatertrust.org
Youzuo LinAssociate Professor at University of North Carolina at Chapel HillPatvirtintas el. paštas unc.edu

Stebėti

Daniel O'Malley

Los Alamos National Laboratory

Patvirtintas el. paštas lanl.gov - Pagrindinis puslapis

applied mathematics machine learning computational science quantum computing


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Quantum algorithm implementations for beginners PJ Coles, S Eidenbenz, S Pakin, A Adedoyin, J Ambrosiano, P Anisimov, ... arXiv, arXiv: 1804.03719, 2018	306*	2018
Understanding hydraulic fracturing: a multi-scale problem JD Hyman, J Jiménez-Martínez, HS Viswanathan, JW Carey, ML Porter, ... Philosophical Transactions of the Royal Society A: Mathematical, Physical …, 2016	152	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
Theory and applications of macroscale models in porous media I Battiato, PT Ferrero V, D O’Malley, CT Miller, PS Takhar, ... Transport in Porous Media 130, 5-76, 2019	96	2019
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
Predictive modeling of dynamic fracture growth in brittle materials with machine learning BA Moore, E Rougier, D O’Malley, G Srinivasan, A Hunter, H Viswanathan Computational Materials Science 148, 46-53, 2018	85	2018
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks T Kadeethum, D O’Malley, JN Fuhg, Y Choi, J Lee, HS Viswanathan, ... Nature Computational Science 1 (12), 819-829, 2021	83	2021
Modeling flow and transport in fracture networks using graphs S Karra, D O'Malley, JD Hyman, HS Viswanathan, G Srinivasan Physical Review E 97 (3), 033304, 2018	80	2018
Where does water go during hydraulic fracturing? D O'Malley, S Karra, RP Currier, N Makedonska, JD Hyman, ... Groundwater 54 (4), 488-497, 2016	72	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
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques T Kadeethum, F Ballarin, Y Choi, D O’Malley, H Yoon, N Bouklas Advances in Water Resources 160, 104098, 2022	61	2022
Quantifying topological uncertainty in fractured systems using graph theory and machine learning G Srinivasan, JD Hyman, DA Osthus, BA Moore, D O’Malley, S Karra, ... Scientific reports 8 (1), 11665, 2018	56	2018
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
Advancing graph‐based algorithms for predicting flow and transport in fractured rock HS Viswanathan, JD Hyman, S Karra, D O'Malley, S Srinivasan, ... Water Resources Research 54 (9), 6085-6099, 2018	49	2018
Anomalous diffusion as modeled by a nonstationary extension of Brownian motion JH Cushman, D O’Malley, M Park Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 79 (3 …, 2009	49	2009
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
Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications A Hunter, BA Moore, M Mudunuru, V Chau, R Tchoua, C Nyshadham, ... Computational Materials Science 157, 87-98, 2019	46	2019
An approach to quantum-computational hydrologic inverse analysis D O’Malley Scientific reports 8 (1), 6919, 2018	46	2018
Reverse annealing for nonnegative/binary matrix factorization J Golden, D O’Malley Plos one 16 (1), e0244026, 2021	45	2021

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