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ORCIDDaniel O'Malley
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- affiliation: Los Alamos National Laboratory, Computational Earth Science Group, NM, USA
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Journal Articles
- 2023
[j11]Sarah Y. Greer, Daniel O'Malley:
Early steps toward practical subsurface computations with quantum computing. Frontiers Comput. Sci. 5 (2023)- [j10]Daniel O'Malley
, Sarah Y. Greer, Aleksandra Pachalieva, Wu Hao, Dylan Robert Harp, Velimir V. Vesselinov:
DPFEHM: a differentiable subsurface physics simulator. J. Open Source Softw. 8(90): 4560 (2023) - [j9]Javier E. Santos, Zachary R. Fox, Arvind Mohan, Daniel O'Malley, Hari S. Viswanathan, Nicholas Lubbers:
Development of the Senseiver for efficient field reconstruction from sparse observations. Nat. Mac. Intell. 5(11): 1317-1325 (2023) - 2022
- [j8]Teeratorn Kadeethum, Daniel O'Malley, Youngsoo Choi, Hari S. Viswanathan, Nikolaos Bouklas, Hongkyu Yoon:
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. Comput. Geosci. 167: 105212 (2022) - 2021
- [j7]Dylan Robert Harp, Dan O'Malley, Bicheng Yan, Rajesh J. Pawar:
On the feasibility of using physics-informed machine learning for underground reservoir pressure management. Expert Syst. Appl. 178: 115006 (2021) - [j6]Teeratorn Kadeethum, Daniel O'Malley, Jan Niklas Fuhg, Youngsoo Choi, Jonghyun Lee, Hari S. Viswanathan, Nikolaos Bouklas:
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks. Nat. Comput. Sci. 1(12): 819-829 (2021) - [j5]John K. Golden, Daniel O'Malley:
Pre- and post-processing in quantum-computational hydrologic inverse analysis. Quantum Inf. Process. 20(5): 176 (2021) - 2020
- [j4]Nishant Panda, Dave Osthus, Gowri Srinivasan, Daniel O'Malley, Viet T. Chau, Diane Oyen, Humberto Godinez:
Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling. J. Comput. Phys. 420: 109719 (2020) - 2019
- [j3]Velimir V. Vesselinov, Maruti Kumar Mudunuru, Satish Karra, Dan O'Malley, Boian S. Alexandrov:
Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing. J. Comput. Phys. 395: 85-104 (2019) - 2018
- [j2]Elizabeth Qian, Benjamin Peherstorfer, Daniel O'Malley, Velimir V. Vesselinov, Karen Willcox:
Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices. SIAM/ASA J. Uncertain. Quantification 6(2): 683-706 (2018) - 2014
- [j1]Daniel O'Malley, Velimir V. Vesselinov:
A Combined Probabilistic/Nonprobabilistic Decision Analysis for Contaminant Remediation. SIAM/ASA J. Uncertain. Quantification 2(1): 607-621 (2014)
Conference and Workshop Papers
- 2023
- [c12]John K. Golden, Andreas Bärtschi, Daniel O'Malley, Stephan J. Eidenbenz:
The Quantum Alternating Operator Ansatz for Satisfiability Problems. QCE 2023: 307-312 - [c11]John K. Golden, Andreas Bärtschi, Daniel O'Malley, Stephan J. Eidenbenz:
Numerical Evidence for Exponential Speed-Up of QAOA over Unstructured Search for Approximate Constrained Optimization. QCE 2023: 496-505 - [c10]John K. Golden, Andreas Bärtschi, Dan O'Malley, Elijah Pelofske, Stephan J. Eidenbenz:
JuliQAOA: Fast, Flexible QAOA Simulation. SC Workshops 2023: 1454-1459 - 2021
- [c9]Jessie M. Henderson, Daniel O'Malley, Hari S. Viswanathan:
Interrogating the performance of quantum annealing for the solution of steady-state subsurface flow. HPEC 2021: 1-6 - [c8]Elijah Pelofske, Georg Hahn, Daniel O'Malley, Hristo N. Djidjev, Boian S. Alexandrov:
Boolean Hierarchical Tucker Networks on Quantum Annealers. LSSC 2021: 351-358 - [c7]John K. Golden, Andreas Bärtschi, Daniel O'Malley, Stephan J. Eidenbenz:
Threshold-Based Quantum Optimization. QCE 2021: 137-147 - [c6]Elijah Pelofske, John K. Golden, Andreas Bärtschi, Daniel O'Malley, Stephan J. Eidenbenz:
Sampling on NISQ Devices: "Who's the Fairest One of All?". QCE 2021: 207-217 - 2020
- [c5]Maruti Kumar Mudunuru, Daniel O'Malley, Shriram Srinivasan, Jeffrey D. Hyman, Matthew R. Sweeney, Luke Frash, Bill Carey, Michael R. Gross, Nathan J. Welch, Satish Karra, Velimir V. Vesselinov, Qinjun Kang, Hongwu Xu, Rajesh J. Pawar, Tim Carr, Liwei Li, George D. Guthrie, Hari S. Viswanathan:
Physics-Informed Machine Learning for Real-time Reservoir Management. AAAI Spring Symposium: MLPS 2020 - [c4]Daniel O'Malley, John K. Golden:
Homomorphic Encryption for Quantum Annealing with Spin Reversal Transformations. HPEC 2020: 1-6 - [c3]Daniel O'Malley, Hristo N. Djidjev, Boian S. Alexandrov:
Tucker-1 Boolean Tensor Factorization with Quantum Annealers. ICRC 2020: 58-65 - 2017
- [c2]Hristo N. Djidjev, Daniel O'Malley, Hari S. Viswanathan, Jeffrey D. Hyman, Satish Karra, Gowri Srinivasan:
Learning on Graphs for Predictions of Fracture Propagation, Flow and Transport. IPDPS Workshops 2017: 1532-1539 - 2016
- [c1]Daniel O'Malley, Velimir V. Vesselinov:
ToQ.jl: A high-level programming language for D-Wave machines based on Julia. HPEC 2016: 1-7
Informal and Other Publications
- 2023
- [i28]John K. Golden, Andreas Bärtschi, Daniel O'Malley, Stephan J. Eidenbenz:
The Quantum Alternating Operator Ansatz for Satisfiability Problems. CoRR abs/2301.11292 (2023) - [i27]Teeratorn Kadeethum, Daniel O'Malley, Youngsoo Choi, Hari S. Viswanathan, Hongkyu Yoon:
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer. CoRR abs/2310.03770 (2023) - [i26]Aleksei G. Sorokin, Aleksandra Pachalieva, Daniel O'Malley, James M. Hyman, Fred J. Hickernell, Nicolas W. Hengartner:
Computationally Efficient and Error Aware Surrogate Construction for Numerical Solutions of Subsurface Flow Through Porous Media. CoRR abs/2310.13765 (2023) - [i25]Agnese Marcato, Daniel O'Malley, Hari S. Viswanathan, Eric Guiltinan, Javier E. Santos:
Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization. CoRR abs/2312.09176 (2023) - [i24]Aleksandra Pachalieva, Jeffrey D. Hyman, Daniel O'Malley, Hari S. Viswanathan, Gowri Srinivasan:
Learning the Factors Controlling Mineralization for Geologic Carbon Sequestration. CoRR abs/2312.13451 (2023) - [i23]Christo Meriwether Keller, Stephan J. Eidenbenz, Andreas Bärtschi, Daniel O'Malley, John K. Golden, Satyajayant Misra:
Hierarchical Multigrid Ansatz for Variational Quantum Algorithms. CoRR abs/2312.15048 (2023) - 2022
- [i22]John K. Golden, Andreas Bärtschi, Stephan J. Eidenbenz, Daniel O'Malley:
Evidence for Super-Polynomial Advantage of QAOA over Unstructured Search. CoRR abs/2202.00648 (2022) - [i21]Marta D'Elia, Hang Deng, Cedric G. Fraces, Krishna C. Garikipati, Lori Graham-Brady, Amanda A. Howard, George Em Karniadakis, Vahid Keshavarzzadeh, Robert M. Kirby, J. Nathan Kutz, Chunhui Li, Xing Liu, Hannah Lu, Pania Newell, Daniel O'Malley, Masa Prodanovic, Gowri Srinivasan, Alexandre M. Tartakovsky, Daniel M. Tartakovsky, Hamdi A. Tchelepi, Bozo Vazic, Hari S. Viswanathan, Hongkyu Yoon, Piotr Zarzycki:
Machine Learning in Heterogeneous Porous Materials. CoRR abs/2202.04137 (2022) - [i20]Teeratorn Kadeethum, Francesco Ballarin, Daniel O'Malley, Youngsoo Choi, Nikolaos Bouklas, Hongkyu Yoon:
Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds. CoRR abs/2202.05460 (2022) - [i19]Aleksandra Pachalieva, Daniel O'Malley, Dylan Robert Harp, Hari S. Viswanathan:
Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management. CoRR abs/2206.10718 (2022) - [i18]Jessie M. Henderson, Marianna Podzorova, M. Cerezo, John K. Golden, Leonard Gleyzer, Hari S. Viswanathan, Daniel O'Malley:
Quantum Algorithms for Geologic Fracture Networks. CoRR abs/2210.11685 (2022) - 2021
- [i17]John K. Golden, Andreas Bärtschi, Daniel O'Malley, Stephan J. Eidenbenz:
QAOA-based Fair Sampling on NISQ Devices. CoRR abs/2101.03258 (2021) - [i16]Elijah Pelofske, Georg Hahn, Daniel O'Malley, Hristo N. Djidjev, Boian S. Alexandrov:
Boolean Hierarchical Tucker Networks on Quantum Annealers. CoRR abs/2103.07399 (2021) - [i15]Teeratorn Kadeethum, Daniel O'Malley, Jan Niklas Fuhg, Youngsoo Choi, Jonghyun Lee, Hari S. Viswanathan, Nikolaos Bouklas:
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks. CoRR abs/2105.13136 (2021) - [i14]John K. Golden, Andreas Bärtschi, Daniel O'Malley, Stephan J. Eidenbenz:
Threshold-Based Quantum Optimization. CoRR abs/2106.13860 (2021) - [i13]Elijah Pelofske, John K. Golden, Andreas Bärtschi, Daniel O'Malley, Stephan J. Eidenbenz:
Sampling on NISQ Devices: "Who's the Fairest One of All?". CoRR abs/2107.06468 (2021) - [i12]Teeratorn Kadeethum, Francesco Ballarin, Youngsoo Choi, Daniel O'Malley, Hongkyu Yoon, Nikolaos Bouklas:
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques. CoRR abs/2107.11460 (2021) - [i11]Elijah Pelofske, Georg Hahn, Daniel O'Malley, Hristo N. Djidjev, Boian S. Alexandrov:
Quantum Annealing Algorithms for Boolean Tensor Networks. CoRR abs/2107.13659 (2021) - [i10]Teeratorn Kadeethum, Dan O'Malley, Youngsoo Choi, Hari S. Viswanathan, Nikolaos Bouklas, Hongkyu Yoon:
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. CoRR abs/2111.14984 (2021) - 2020
- [i9]John K. Golden, Daniel O'Malley:
Reverse Annealing for Nonnegative/Binary Matrix Factorization. CoRR abs/2007.05565 (2020) - [i8]Daniel O'Malley, John K. Golden:
Homomorphic Encryption for Quantum Annealing with Spin Reversal Transformations. CoRR abs/2009.00111 (2020) - [i7]Cristina Garcia-Cardona, M. Giselle Fernández-Godino, Daniel O'Malley, Tanmoy Bhattacharya:
Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture. CoRR abs/2012.15739 (2020) - 2019
- [i6]Daniel O'Malley, John K. Golden, Velimir V. Vesselinov:
Learning to regularize with a variational autoencoder for hydrologic inverse analysis. CoRR abs/1906.02401 (2019) - [i5]John K. Golden, Daniel O'Malley:
Pre- and post-processing in quantum-computational hydrologic inverse analysis. CoRR abs/1910.00626 (2019) - 2018
- [i4]Patrick J. Coles, Stephan J. Eidenbenz, Scott Pakin, Adetokunbo Adedoyin, John Ambrosiano, Petr M. Anisimov, William Casper, Gopinath Chennupati, Carleton Coffrin, Hristo N. Djidjev, David Gunter, Satish Karra, Nathan Lemons, Shizeng Lin, Andrey Y. Lokhov, Alexander Malyzhenkov, David Dennis Lee Mascarenas, Susan M. Mniszewski, Balu Nadiga, Dan O'Malley, Diane Oyen, Lakshman Prasad, Randy Roberts, Philip Romero, Nandakishore Santhi, Nikolai Sinitsyn, Pieter Swart, Marc Vuffray, Jim Wendelberger, Boram Yoon, Richard J. Zamora, Wei Zhu:
Quantum Algorithm Implementations for Beginners. CoRR abs/1804.03719 (2018) - [i3]Velimir V. Vesselinov, Maruti Kumar Mudunuru, Satish Karra, Dan O'Malley, Boian S. Alexandrov:
Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing. CoRR abs/1805.06454 (2018) - [i2]A. Hunter, Bryan A. Moore, Maruti Kumar Mudunuru, Viet T. Chau, Robyn L. Miller, Roselyne B. Tchoua, C. Nyshadham, Satish Karra, Dan O'Malley, Esteban Rougier, Hari S. Viswanathan, Gowri Srinivasan:
Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications. CoRR abs/1806.01949 (2018) - 2017
- [i1]Daniel O'Malley, Velimir V. Vesselinov, Boian S. Alexandrov, Ludmil B. Alexandrov:
Nonnegative/binary matrix factorization with a D-Wave quantum annealer. CoRR abs/1704.01605 (2017)
Coauthor Index
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