Michael D. Shields
Orcid: 0000-0003-1370-6785
According to our database1,
Michael D. Shields
authored at least 31 papers
between 2014 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification.
CoRR, 2024
Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification.
CoRR, 2024
Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks.
CoRR, 2024
2023
Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling.
J. Comput. Phys., November, 2023
On the influence of over-parameterization in manifold based surrogates and deep neural operators.
J. Comput. Phys., April, 2023
CoRR, 2023
CoRR, 2023
CoRR, 2023
2022
Deep transfer operator learning for partial differential equations under conditional shift.
Nat. Mac. Intell., December, 2022
Grassmannian Diffusion Maps-Based Dimension Reduction and Classification for High-Dimensional Data.
SIAM J. Sci. Comput., 2022
Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation.
Reliab. Eng. Syst. Saf., 2022
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems.
J. Comput. Phys., 2022
J. Comput. Phys., 2022
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation.
CoRR, 2022
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference.
CoRR, 2022
Deep transfer learning for partial differential equations under conditional shift with DeepONet.
CoRR, 2022
2021
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models.
CoRR, 2021
2020
UQpy: A general purpose Python package and development environment for uncertainty quantification.
J. Comput. Sci., 2020
On the quantification and efficient propagation of imprecise probabilities with copula dependence.
Int. J. Approx. Reason., 2020
Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold.
CoRR, 2020
2019
Uncertainty quantification (UQ) as an archetype for research: Integrating UQ into undergraduate research education.
Proceedings of the IEEE Frontiers in Education Conference, 2019
2018
Reliab. Eng. Syst. Saf., 2018
Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations.
J. Comput. Phys., 2018
Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis.
J. Comput. Phys., 2018
2016
2015
Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification.
Reliab. Eng. Syst. Saf., 2015
2014
Mapping model validation metrics to subject matter expert scores for model adequacy assessment.
Reliab. Eng. Syst. Saf., 2014