D. O’Malley

发表

Nonnegative/Binary matrix factorization with a D-Wave quantum annealer

Velimir V. Vesselinov, Ludmil B. Alexandrov, Boian S. Alexandrov, 2017, PloS one.

Machine Learning in Heterogeneous Porous Materials

Cedric G. Fraces, Robert M. Kirby, H. Viswanathan, 2022, ArXiv.

Predictive modeling of dynamic fracture growth in brittle materials with machine learning

Hari S. Viswanathan, Esteban Rougier, Daniel O'Malley, 2018, Computational Materials Science.

Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing

Satish Karra, Maruti Kumar Mudunuru, Velimir V. Vesselinov, 2018, J. Comput. Phys..

Nonnegative tensor factorization for contaminant source identification.

D. O'Malley, V. Vesselinov, B. Alexandrov, 2019, Journal of contaminant hydrology.

Contaminant source identification using semi-supervised machine learning.

D. O'Malley, V. Vesselinov, B. Alexandrov, 2017, Journal of contaminant hydrology.

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks pdf

Hari S. Viswanathan, Daniel O'Malley, Jan N. Fuhg, 2021, Nature Computational Science.

Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties

H. Viswanathan, D. O'Malley, N. Bouklas, 2021, Comput. Geosci..

Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications

Bryan A. Moore, Roselyne B. Tchoua, H. Viswanathan, 2019, Computational Materials Science.

Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices

Benjamin Peherstorfer, Velimir V. Vesselinov, Karen Willcox, 2018, SIAM/ASA J. Uncertain. Quantification.

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques pdf

F. Ballarin, T. Kadeethum, N. Bouklas, 2021, Advances in Water Resources.

Development of robust pressure management strategies for geologic CO2 sequestration

Zunsheng Jiao, Rajesh J. Pawar, Philip H. Stauffer, 2017 .

Robust system size reduction of discrete fracture networks: a multi-fidelity method that preserves transport characteristics

Satish Karra, Shriram Srinivasan, Gowri Srinivasan, 2018, Computational Geosciences.

Advancing Graph‐Based Algorithms for Predicting Flow and Transport in Fractured Rock

Satish Karra, Hari S. Viswanathan, Jeffrey D. Hyman, 2018, Water Resources Research.

Learning to regularize with a variational autoencoder for hydrologic inverse analysis pdf

Daniel O'Malley, Velimir V. Vesselinov, John K. Golden, 2019, ArXiv.

Accelerating high-strain continuum-scale brittle fracture simulations with machine learning

R. Haftka, D. O'Malley, A. Hunter, 2021 .

Accelerating continuum-scale brittle fracture simulations with machine learning

R. Haftka, A. Hunter, K. Larkin, 2020, 2001.11328.

Flyer Plate Continuum Simulations Informed with Machine Learning Crack Evolution

Raphael T. Haftka, Gowri Srinivasan, Diane Oyen, 2020 .

Random Renormalization Group Operators Applied to Stochastic Dynamics

J. H. Cushman, D. O'Malley, D. O’Malley, 2012 .

Robust decision analysis for environmental management of groundwater contamination sites

D. O'Malley, V. Vesselinov, D. Katzman, 2013, 1311.6014.

Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning

Satish Karra, Gowri Srinivasan, Esteban Rougier, 2018, Scientific Reports.

On the feasibility of using physics-informed machine learning for underground reservoir pressure management

Rajesh J. Pawar, Dylan R. Harp, Daniel O'Malley, 2021, Expert Syst. Appl..

Ubiquity of, and geostatistics for, nonstationary increment random fields

J. H. Cushman, D. O’Malley, 2013 .

Quantum annealing algorithms for Boolean tensor networks

H. Djidjev, Georg Hahn, B. Alexandrov, 2021, Scientific Reports.

Reverse annealing for nonnegative/binary matrix factorization

John K. Golden, D. O’Malley, 2020, PloS one.

Boolean Hierarchical Tucker Networks on Quantum Annealers pdf

Georg Hahn, Boian S. Alexandrov, Daniel O'Malley, 2021, LSSC.

Tucker-1 Boolean Tensor Factorization with Quantum Annealers

H. Djidjev, D. O'Malley, B. Alexandrov, 2020, 2020 International Conference on Rebooting Computing (ICRC).

Achieving fair sampling in quantum annealing

C. Nehrkorn, Vaibhaw Kumar, Casey Tomlin, 2020, 2007.08487.

Sampling on NISQ Devices: "Who’s the Fairest One of All?"

John K. Golden, D. O'Malley, S. Eidenbenz, 2021, 2021 IEEE International Conference on Quantum Computing and Engineering (QCE).

Fickian dispersion is anomalous

John H. Cushman, Daniel O'Malley, J. H. Cushman, 2015 .

QAOA-based Fair Sampling on NISQ Devices pdf

Stephan Eidenbenz, Andreas Bärtschi, Daniel O'Malley, 2021, ArXiv.

Push‐pull tracer tests: Their information content and use for characterizing non‐Fickian, mobile‐immobile behavior

D. O'Malley, S. Karra, B. Berkowitz, 2016 .

Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture

Cristina Garcia-Cardona, M. G. Fern'andez-Godino, T. Bhattacharya, 2020, Computational Materials Science.

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Gowri Srinivasan, Diane Oyen, Dave Osthus, 2020, J. Comput. Phys..

Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications pdf

Satish Karra, Hari S. Viswanathan, Gowri Srinivasan, 2018, ArXiv.

Decision analysis for robust CO 2 injection: Application of Bayesian-Information-Gap Decision Theory

Velimir V. Vesselinov, Matthew Grasinger, Satish Karra, 2015 .

Quantum Algorithm Implementations for Beginners pdf

Scott Pakin, Carleton Coffrin, Gopinath Chennupati, 2018, ACM Transactions on Quantum Computing.

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

H. Viswanathan, D. O'Malley, D. Harp, 2022, Scientific Reports.

Active layer hydrology in an arctic tundra ecosystem: quantifying water sources and cycling using water stable isotopes

S. Wullschleger, V. Vesselinov, G. Perkins, 2016 .

Diffusive mixing and Tsallis entropy.

J. H. Cushman, V. Vesselinov, D. O’Malley, 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

Threshold-Based Quantum Optimization

Stephan Eidenbenz, Andreas Bärtschi, Daniel O'Malley, 2021, 2021 IEEE International Conference on Quantum Computing and Engineering (QCE).

Large‐scale inverse model analyses employing fast randomized data reduction

Velimir V. Vesselinov, Tan Bui-Thanh, Ellen B. Le, 2017 .

A Method for Identifying Diffusive Trajectories with Stochastic Models

J. H. Cushman, V. Vesselinov, D. O’Malley, 2014 .

Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds

Youngsoo Choi, F. Ballarin, N. Bouklas, 2022, Proposed for presentation at the InterPore 2022 in ,.

A stochastic model for anomalous diffusion in confined nano-films near a strain-induced critical point

J. H. Cushman, Moongyu Park, D. O’Malley, 2011 .

Homomorphic Encryption for Quantum Annealing with Spin Reversal Transformations

Daniel O'Malley, John K. Golden, D. O’Malley, 2020, 2020 IEEE High Performance Extreme Computing Conference (HPEC).

J. H. Cushman, D. O’Malley, M. Park, 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

Two-scale renormalization-group classification of diffusive processes.

Daniel O'Malley, J. H. Cushman, John H Cushman, 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

A Renormalization Group Classification of Nonstationary and/or Infinite Second Moment Diffusive Processes

John H. Cushman, Daniel O’Malley, J. H. Cushman, 2012 .

Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

G. Beroza, A. Curtis, Y. Li, 2023, Journal of Geophysical Research: Solid Earth.

Agent-Based Socio-Hydrological Hybrid Modeling for Water Resource Management

Velimir V. Vesselinov, Daniel O’Malley, Joseph Bakarji, 2017, Water Resources Management.

Learning on Graphs for Predictions of Fracture Propagation, Flow and Transport

Satish Karra, Hristo Djidjev, Jeffrey D. Hyman, 2017, 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

Reducing uncertainty associated with CO2 injection and brine production in heterogeneous formations

Rajesh J. Pawar, David Dempsey, Daniel O'Malley, 2015 .

Pre- and post-processing in quantum-computational hydrologic inverse analysis

John K. Golden, D. O’Malley, 2019, Quantum Information Processing.

A Physics-informed Machine Learning Workflow to Forecast Production in a Fractured Marcellus Shale Reservoir

H. Viswanathan, L. Frash, S. Karra, 2021, Proceedings of the 9th Unconventional Resources Technology Conference.

A machine learning framework for rapid forecasting and history matching in unconventional reservoirs

H. Viswanathan, L. Frash, J. Carey, 2021, Scientific Reports.

A Computationally Efficient Parallel Levenberg-Marquardt Algorithm for Large-Scale Big-Data Inversion

Youzuo Lin, V. V. Vesselinov, D. O'Malley, 2015 .

ToQ.jl: A high-level programming language for D-Wave machines based on Julia

Velimir V. Vesselinov, Daniel O'Malley, V. Vesselinov, 2016, 2016 IEEE High Performance Extreme Computing Conference (HPEC).

A universal field equation for dispersive processes in heterogeneous media

J. H. Cushman, Moongyu Park, M. Moroni, 2011 .

A computationally efficient parallel Levenberg‐Marquardt algorithm for highly parameterized inverse model analyses

Velimir V. Vesselinov, Daniel O'Malley, Youzuo Lin, 2016 .

Towards real-time forecasting of natural gas production by harnessing graph theory for stochastic discrete fracture networks

Satish Karra, Jeffrey D. Hyman, Nataliia Makedonska, 2020 .

Scaling laws for fractional Brownian motion with power-law clock

J. H. Cushman, D. O’Malley, G. Johnson, 2011 .

Anomalous diffusion as modeled by a nonstationary extension of Brownian motion.

J. H. Cushman, Moongyu Park, D. O’Malley, 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

Physics-Informed Machine Learning for Real-time Reservoir Management

Satish Karra, Jeffrey D. Hyman, Hari S. Viswanathan, 2020, AAAI Spring Symposium: MLPS.