| Quantum algorithm implementations for beginners A Adedoyin, J Ambrosiano, P Anisimov, A Bärtschi, W Casper, ... arXiv preprint arXiv:1804.03719, 2018 | 160 | 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 | 118 | 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 | 99 | 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 | 73 | 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 | 70 | 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 | 63 | 2016 |
| 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 | 61 | 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 | 52 | 2018 |
| Quantum algorithm implementations for beginners J Abhijith, A Adedoyin, J Ambrosiano, P Anisimov, A Bärtschi, W Casper, ... arXiv e-prints, arXiv: 1804.03719, 2018 | 51 | 2018 |
| Contaminant source identification using semi-supervised machine learning VV Vesselinov, BS Alexandrov, D O’Malley Journal of contaminant hydrology 212, 134-142, 2018 | 50 | 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 | 49 | 2019 |
| 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 | 43 | 2018 |
| Anomalous diffusion as modeled by a nonstationary extension of Brownian motion JH Cushman, D O’Malley, M Park Physical Review E 79 (3), 032101, 2009 | 42 | 2009 |
| 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 | 40 | 2021 |
| Fickian dispersion is anomalous JH Cushman, D O’Malley Journal of Hydrology 531, 161-167, 2015 | 40 | 2015 |
| 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 | 38 | 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 | 37 | 2018 |
| An approach to quantum-computational hydrologic inverse analysis D O’Malley Scientific reports 8 (1), 1-9, 2018 | 37 | 2018 |
| 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 | 35 | 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 | 32 | 2019 |
Velimir V Vesselinov ("monty")SmartTensors LLC, Santa Fe, New Mexico, USAVerified email at smarttensors.com
