Daniel O'Malley

Orcid: 0000-0003-0432-3088

Affiliations:
  • Los Alamos National Laboratory, Computational Earth Science Group, NM, USA


According to our database1, Daniel O'Malley authored at least 45 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction.
CoRR, 2024

Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation.
CoRR, 2024

Information Extraction from Historical Well Records Using A Large Language Model.
CoRR, 2024

Hierarchical Multigrid Ansatz for Variational Quantum Algorithms.
Proceedings of the ISC High Performance 2024 Research Paper Proceedings (39th International Conference), 2024

2023
Development of the Senseiver for efficient field reconstruction from sparse observations.
Nat. Mac. Intell., October, 2023

DPFEHM: a differentiable subsurface physics simulator.
J. Open Source Softw., October, 2023

Early steps toward practical subsurface computations with quantum computing.
Frontiers Comput. Sci., 2023

Learning the Factors Controlling Mineralization for Geologic Carbon Sequestration.
CoRR, 2023

Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization.
CoRR, 2023

Computationally Efficient and Error Aware Surrogate Construction for Numerical Solutions of Subsurface Flow Through Porous Media.
CoRR, 2023

Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer.
CoRR, 2023

JuliQAOA: Fast, Flexible QAOA Simulation.
Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, 2023

Numerical Evidence for Exponential Speed-Up of QAOA over Unstructured Search for Approximate Constrained Optimization.
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

The Quantum Alternating Operator Ansatz for Satisfiability Problems.
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

2022
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties.
Comput. Geosci., 2022

Quantum Algorithms for Geologic Fracture Networks.
CoRR, 2022

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management.
CoRR, 2022

Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds.
CoRR, 2022

Machine Learning in Heterogeneous Porous Materials.
CoRR, 2022

Evidence for Super-Polynomial Advantage of QAOA over Unstructured Search.
CoRR, 2022

2021
Pre- and post-processing in quantum-computational hydrologic inverse analysis.
Quantum Inf. Process., 2021

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks.
Nat. Comput. Sci., 2021

On the feasibility of using physics-informed machine learning for underground reservoir pressure management.
Expert Syst. Appl., 2021

Quantum Annealing Algorithms for Boolean Tensor Networks.
CoRR, 2021

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques.
CoRR, 2021

QAOA-based Fair Sampling on NISQ Devices.
CoRR, 2021

Sampling on NISQ Devices: "Who's the Fairest One of All?".
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2021

Threshold-Based Quantum Optimization.
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2021

Boolean Hierarchical Tucker Networks on Quantum Annealers.
Proceedings of the Large-Scale Scientific Computing - 13th International Conference, 2021

Interrogating the performance of quantum annealing for the solution of steady-state subsurface flow.
Proceedings of the 2021 IEEE High Performance Extreme Computing Conference, 2021

2020
Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling.
J. Comput. Phys., 2020

Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture.
CoRR, 2020

Reverse Annealing for Nonnegative/Binary Matrix Factorization.
CoRR, 2020

Tucker-1 Boolean Tensor Factorization with Quantum Annealers.
Proceedings of the International Conference on Rebooting Computing, 2020

Homomorphic Encryption for Quantum Annealing with Spin Reversal Transformations.
Proceedings of the 2020 IEEE High Performance Extreme Computing Conference, 2020

Physics-Informed Machine Learning for Real-time Reservoir Management.
Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 23rd - to, 2020

2019
Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing.
J. Comput. Phys., 2019

Learning to regularize with a variational autoencoder for hydrologic inverse analysis.
CoRR, 2019

2018
Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices.
SIAM/ASA J. Uncertain. Quantification, 2018

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

Quantum Algorithm Implementations for Beginners.
CoRR, 2018

2017
Nonnegative/binary matrix factorization with a D-Wave quantum annealer.
CoRR, 2017

Learning on Graphs for Predictions of Fracture Propagation, Flow and Transport.
Proceedings of the 2017 IEEE International Parallel and Distributed Processing Symposium Workshops, 2017

2016
ToQ.jl: A high-level programming language for D-Wave machines based on Julia.
Proceedings of the 2016 IEEE High Performance Extreme Computing Conference, 2016

2014
A Combined Probabilistic/Nonprobabilistic Decision Analysis for Contaminant Remediation.
SIAM/ASA J. Uncertain. Quantification, 2014


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