A Combined Probabilistic/Nonprobabilistic Decision Analysis for Contaminant Remediation. - Daniel O'Malley; Velimir V. Vesselinov (SIAM/ASA J. Uncertain. Quantification, 2014)

ToQ.jl: A high-level programming language for D-Wave machines based on Julia. - Daniel O'Malley; Velimir V. Vesselinov (HPEC, 2016)

Learning on Graphs for Predictions of Fracture Propagation, Flow and Transport. - Hristo N. Djidjev; Daniel O'Malley; Hari S. Viswanathan; Jeffrey D. Hyman; Satish Karra; Gowri Srinivasan (IPDPS Workshops, 2017)

Nonnegative/binary matrix factorization with a D-Wave quantum annealer. - Daniel O'Malley; Velimir V. Vesselinov; Boian S. Alexandrov; Ludmil B. Alexandrov (CoRR, 2017)

Quantum Algorithm Implementations for Beginners. - 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; et al. (CoRR, 2018)

Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications. - 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 (CoRR, 2018)

Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices. - Elizabeth Qian; Benjamin Peherstorfer; Daniel O'Malley; Velimir V. Vesselinov; Karen Willcox (SIAM/ASA J. Uncertain. Quantification, 2018)

Learning to regularize with a variational autoencoder for hydrologic inverse analysis. - Daniel O'Malley; John K. Golden; Velimir V. Vesselinov (CoRR, 2019)

Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing. - Velimir V. Vesselinov; Maruti Kumar Mudunuru; Satish Karra; Dan O'Malley; Boian S. Alexandrov (J. Comput. Phys., 2019)

Physics-Informed Machine Learning for Real-time Reservoir Management. - 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; et al. (AAAI Spring Symposium: MLPS, 2020)

Homomorphic Encryption for Quantum Annealing with Spin Reversal Transformations. - Daniel O'Malley; John K. Golden (HPEC, 2020)

Tucker-1 Boolean Tensor Factorization with Quantum Annealers. - Daniel O'Malley; Hristo N. Djidjev; Boian S. Alexandrov (ICRC, 2020)

Reverse Annealing for Nonnegative/Binary Matrix Factorization. - John K. Golden; Daniel O'Malley (CoRR, 2020)

Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture. - Cristina Garcia-Cardona; M. Giselle Fernández-Godino; Daniel O'Malley; Tanmoy Bhattacharya 001 (CoRR, 2020)

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling. - Nishant Panda; Dave Osthus; Gowri Srinivasan; Daniel O'Malley; Viet T. Chau; Diane Oyen; Humberto Godinez (J. Comput. Phys., 2020)

Interrogating the performance of quantum annealing for the solution of steady-state subsurface flow. - Jessie M. Henderson; Daniel O'Malley; Hari S. Viswanathan (HPEC, 2021)

Boolean Hierarchical Tucker Networks on Quantum Annealers. - Elijah Pelofske; Georg Hahn; Daniel O'Malley; Hristo N. Djidjev; Boian S. Alexandrov (LSSC, 2021)

Threshold-Based Quantum Optimization. - John K. Golden; Andreas Bärtschi; Daniel O'Malley; Stephan J. Eidenbenz (QCE, 2021)

Sampling on NISQ Devices: "Who's the Fairest One of All?". - Elijah Pelofske; John K. Golden; Andreas Bärtschi; Daniel O'Malley; Stephan J. Eidenbenz (QCE, 2021)

QAOA-based Fair Sampling on NISQ Devices. - John K. Golden; Andreas Bärtschi; Daniel O'Malley; Stephan J. Eidenbenz (CoRR, 2021)

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques. - Teeratorn Kadeethum; Francesco Ballarin; Youngsoo Choi; Daniel O'Malley; Hongkyu Yoon; Nikolaos Bouklas (CoRR, 2021)

Quantum Annealing Algorithms for Boolean Tensor Networks. - Elijah Pelofske; Georg Hahn; Daniel O'Malley; Hristo N. Djidjev; Boian S. Alexandrov (CoRR, 2021)

On the feasibility of using physics-informed machine learning for underground reservoir pressure management. - Dylan Robert Harp; Dan O'Malley; Bicheng Yan; Rajesh J. Pawar (Expert Syst. Appl., 2021)

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks. - Teeratorn Kadeethum; Daniel O'Malley; Jan Niklas Fuhg; Youngsoo Choi; Jonghyun Lee 005; Hari S. Viswanathan; Nikolaos Bouklas (Nat. Comput. Sci., 2021)

Pre- and post-processing in quantum-computational hydrologic inverse analysis. - John K. Golden; Daniel O'Malley (Quantum Inf. Process., 2021)

Evidence for Super-Polynomial Advantage of QAOA over Unstructured Search. - John K. Golden; Andreas Bärtschi; Stephan J. Eidenbenz; Daniel O'Malley (CoRR, 2022)

Machine Learning in Heterogeneous Porous Materials. - 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; et al. (CoRR, 2022)

Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds. - Teeratorn Kadeethum; Francesco Ballarin; Daniel O'Malley; Youngsoo Choi; Nikolaos Bouklas; Hongkyu Yoon (CoRR, 2022)

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management. - Aleksandra Pachalieva; Daniel O'Malley; Dylan Robert Harp; Hari S. Viswanathan (CoRR, 2022)

Quantum Algorithms for Geologic Fracture Networks. - Jessie M. Henderson; Marianna Podzorova; M. Cerezo; John K. Golden; Leonard Gleyzer; Hari S. Viswanathan; Daniel O'Malley (CoRR, 2022)

Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. - Teeratorn Kadeethum; Daniel O'Malley; Youngsoo Choi; Hari S. Viswanathan; Nikolaos Bouklas; Hongkyu Yoon (Comput. Geosci., 2022)

The Quantum Alternating Operator Ansatz for Satisfiability Problems. - John K. Golden; Andreas Bärtschi; Daniel O'Malley; Stephan J. Eidenbenz (QCE, 2023)

Numerical Evidence for Exponential Speed-Up of QAOA over Unstructured Search for Approximate Constrained Optimization. - John K. Golden; Andreas Bärtschi; Daniel O'Malley; Stephan J. Eidenbenz (QCE, 2023)

JuliQAOA: Fast, Flexible QAOA Simulation. - John K. Golden; Andreas Bärtschi; Dan O'Malley; Elijah Pelofske; Stephan J. Eidenbenz (SC Workshops, 2023)

Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer. - Teeratorn Kadeethum; Daniel O'Malley; Youngsoo Choi; Hari S. Viswanathan; Hongkyu Yoon (CoRR, 2023)

Computationally Efficient and Error Aware Surrogate Construction for Numerical Solutions of Subsurface Flow Through Porous Media. - Aleksei G. Sorokin; Aleksandra Pachalieva; Daniel O'Malley; James M. Hyman; Fred J. Hickernell; Nicolas W. Hengartner (CoRR, 2023)

Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization. - Agnese Marcato; Daniel O'Malley; Hari S. Viswanathan; Eric Guiltinan; Javier E. Santos (CoRR, 2023)

Learning the Factors Controlling Mineralization for Geologic Carbon Sequestration. - Aleksandra Pachalieva; Jeffrey D. Hyman; Daniel O'Malley; Hari S. Viswanathan; Gowri Srinivasan (CoRR, 2023)

Early steps toward practical subsurface computations with quantum computing. - Sarah Y. Greer; Daniel O'Malley (Frontiers Comput. Sci., 2023)

DPFEHM: a differentiable subsurface physics simulator. - Daniel O'Malley; Sarah Y. Greer; Aleksandra Pachalieva; Wu Hao; Dylan Robert Harp; Velimir V. Vesselinov (J. Open Source Softw., 2023)

Development of the Senseiver for efficient field reconstruction from sparse observations. - Javier E. Santos; Zachary R. Fox; Arvind T. Mohan; Daniel O'Malley; Hari S. Viswanathan; Nicholas Lubbers (Nat. Mac. Intell., 2023)

Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation. - Manish Bhattarai; Javier E. Santos; Shawn Jones; Ayan Biswas; Boian S. Alexandrov; Daniel O'Malley (HPEC, 2024)

Hierarchical Multigrid Ansatz for Variational Quantum Algorithms. - Christo Meriwether Keller; Stephan J. Eidenbenz; Andreas Bärtschi; Daniel O'Malley; John K. Golden; Satyajayant Misra (ISC, 2024)

Information Extraction from Historical Well Records Using A Large Language Model. - Zhiwei Ma; Javier E. Santos; Greg Lackey; Hari S. Viswanathan; Daniel O'Malley (CoRR, 2024)

Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction. - Paul Schwerdtner; Prakash Mohan 004; Aleksandra Pachalieva; Julie Bessac; Daniel O'Malley; Benjamin Peherstorfer (CoRR, 2024)

Benchmarking Large Language Models with Integer Sequence Generation Tasks. - Daniel O'Malley; Manish Bhattarai; Javier E. Santos (CoRR, 2024)

Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation. - Manish Bhattarai; Minh Vu; Javier E. Santos; Ismael Boureima; Daniel O'Malley (CoRR, 2024)

Model-Agnostic Knowledge Guided Correction for Improved Neural Surrogate Rollout. - Bharat Srikishan; Daniel O'Malley; Mohamed Mehana; Nicholas Lubbers; Nikhil Muralidhar (ICLR, 2025)

PatchFinder: Leveraging Visual Language Models for Accurate Information Retrieval Using Model Uncertainty. - Roman Colman; Minh Vu; Manish Bhattarai; Martin Ma; Hari S. Viswanathan; Daniel O'Malley; Javier E. Santos (WACV, 2025)

LLM-Assisted Translation of Legacy FORTRAN Codes to C++: A Cross-Platform Study. - Nishath Rajiv Ranasinghe; Shawn M. Jones; Michal Kucer; Ayan Biswas; Daniel O'Malley; Alexander Most; Selma Liliane Wanna; Ajay Sreekumar (CoRR, 2025)

ARCS: Agentic Retrieval-Augmented Code Synthesis with Iterative Refinement. - Manish Bhattarai; Miguel Cordova; Javier E. Santos; Dan O'Malley (CoRR, 2025)

Lost in OCR Translation? Vision-Based Approaches to Robust Document Retrieval. - Alexander Most; Joseph Winjum; Ayan Biswas; Shawn M. Jones; Nishath Rajiv Ranasinghe; Dan O'Malley; Manish Bhattarai (CoRR, 2025)

A Foundation Model for Material Fracture Prediction. - Agnese Marcato; Aleksandra Pachalieva; Ryley G. Hill; Kai Gao; Xiaoyu Wang; Esteban Rougier; Zhou Lei; Vinamra Agrawal; Janel Chua; Qinjun Kang; Jeffrey D. Hyman; Abigail Hunter; et al. (CoRR, 2025)