Daniel O'Malley
Orcid: 0000-0003-0432-3088Affiliations:
- Los Alamos National Laboratory, Computational Earth Science Group, NM, USA
According to our database1,
Daniel O'Malley
authored at least 42 papers
between 2014 and 2023.
Collaborative distances:
Collaborative distances:
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Bibliography
2023
Development of the Senseiver for efficient field reconstruction from sparse observations.
Nat. Mac. Intell., October, 2023
J. Open Source Softw., October, 2023
Frontiers Comput. Sci., 2023
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
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
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
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
2021
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
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques.
CoRR, 2021
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2021
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2021
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
Proceedings of the International Conference on Rebooting Computing, 2020
Proceedings of the 2020 IEEE High Performance Extreme Computing Conference, 2020
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
SIAM/ASA J. Uncertain. Quantification, 2018
Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications.
CoRR, 2018
2017
Proceedings of the 2017 IEEE International Parallel and Distributed Processing Symposium Workshops, 2017
2016
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