Quantum algorithm implementations for beginners PJ Coles, S Eidenbenz, S Pakin, A Adedoyin, J Ambrosiano, P Anisimov, ... arXiv, arXiv: 1804.03719, 2018 | 296* | 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 | 144 | 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 | 138 | 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 | 86 | 2019 |
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 | 84 | 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 | 83 | 2016 |
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 | 79 | 2018 |
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 | 75 | 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 | 71 | 2021 |
Where does water go during hydraulic fracturing? D O'Malley, S Karra, RP Currier, N Makedonska, JD Hyman, ... Groundwater 54 (4), 488-497, 2016 | 69 | 2016 |
Contaminant source identification using semi-supervised machine learning VV Vesselinov, BS Alexandrov, D O’Malley Journal of contaminant hydrology 212, 134-142, 2018 | 67 | 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 | 57 | 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 | 49 | 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 | 47 | 2019 |
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 | 47 | 2009 |
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 |
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 | 44 | 2018 |
On the feasibility of using physics-informed machine learning for underground reservoir pressure management DR Harp, D O’Malley, B Yan, R Pawar Expert Systems with Applications 178, 115006, 2021 | 43 | 2021 |
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 | 43 | 2016 |