Daniel O'Malley
Zitiert von
Zitiert von
Quantum algorithm implementations for beginners
PJ Coles, S Eidenbenz, S Pakin, A Adedoyin, J Ambrosiano, P Anisimov, ...
arXiv, arXiv: 1804.03719, 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
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
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
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
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
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
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
Where does water go during hydraulic fracturing?
D O'Malley, S Karra, RP Currier, N Makedonska, JD Hyman, ...
Groundwater 54 (4), 488-497, 2016
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
Contaminant source identification using semi-supervised machine learning
VV Vesselinov, BS Alexandrov, D O’Malley
Journal of contaminant hydrology 212, 134-142, 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
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
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
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
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
An approach to quantum-computational hydrologic inverse analysis
D O’Malley
Scientific reports 8 (1), 6919, 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
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
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
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