Michael D. Shields

According to our database, Michael D. Shields authored at least 35 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Simulation of non-Gaussian wind field as a 3rd-order stochastic wave.

Lohit Vandanapu, Michael D. Shields

Reliab. Eng. Syst. Saf., 2024

On active learning for Gaussian process-based global sensitivity analysis.

Mohit Chauhan, Mariel Ojeda-Tuz, Ryan Catarelli, Kurtis R. Gurley, Dimitrios Tsapetis, Michael D. Shields

Reliab. Eng. Syst. Saf., 2024

Physics-informed polynomial chaos expansions.

Lukás Novák, Himanshu Sharma, Michael D. Shields

J. Comput. Phys., 2024

Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification.

Dimitris G. Giovanis, Dimitrios Loukrezis, Ioannis G. Kevrekidis, Michael D. Shields

J. Comput. Phys., 2024

Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving.

Varun Kumar, Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis

CoRR, 2024

A Resolution Independent Neural Operator.

Bahador Bahmani, Somdatta Goswami, Ioannis G. Kevrekidis, Michael D. Shields

CoRR, 2024

Bayesian neural networks for predicting uncertainty in full-field material response.

George D. Pasparakis, Lori Graham-Brady, Michael D. Shields

CoRR, 2024

Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification.

Himanshu Sharma, Lukás Novák, Michael D. Shields

CoRR, 2024

Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks.

Denny Thaler, Somayajulu L. N. Dhulipala, Franz Bamer, Bernd Markert, Michael D. Shields

CoRR, 2024

2023

UQpy v4.1: Uncertainty quantification with Python.

Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Lukás Novák, Promit Chakroborty, Himanshu Sharma, Mohit Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner

SoftwareX, December, 2023

Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling.

Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields

J. Comput. Phys., November, 2023

On the influence of over-parameterization in manifold based surrogates and deep neural operators.

Katiana Kontolati, Somdatta Goswami, Michael D. Shields, George Em Karniadakis

J. Comput. Phys., April, 2023

Learning in latent spaces improves the predictive accuracy of deep neural operators.

Katiana Kontolati, Somdatta Goswami, George Em Karniadakis, Michael D. Shields

CoRR, 2023

Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion.

Lukás Novák, Michael D. Shields, Václav Sadílek, Miroslav Vorechovský

CoRR, 2023

2022

Deep transfer operator learning for partial differential equations under conditional shift.

Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis

Nat. Mac. Intell., December, 2022

Grassmannian Diffusion Maps-Based Dimension Reduction and Classification for High-Dimensional Data.

Ketson R. M. dos Santos, Dimitrios G. Giovanis, Michael D. Shields

SIAM J. Sci. Comput., 2022

Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation.

Somayajulu L. N. Dhulipala, Michael D. Shields, Promit Chakroborty, Wen Jiang, Benjamin W. Spencer, Jason D. Hales, Vincent M. Laboure, Zachary M. Prince, Chandrakanth Bolisetti, Yifeng Che

Reliab. Eng. Syst. Saf., 2022

A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems.

Katiana Kontolati, Dimitrios Loukrezis, Dimitrios G. Giovanis, Lohit Vandanapu, Michael D. Shields

J. Comput. Phys., 2022

Active learning with multifidelity modeling for efficient rare event simulation.

Somayajulu L. N. Dhulipala, Michael D. Shields, Benjamin W. Spencer, Chandrakanth Bolisetti, Andrew E. Slaughter, Vincent M. Laboure, Promit Chakroborty

J. Comput. Phys., 2022

General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation.

Promit Chakroborty, Somayajulu L. N. Dhulipala, Yifeng Che, Wen Jiang, Benjamin W. Spencer, Jason D. Hales, Michael D. Shields

CoRR, 2022

Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference.

Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields

CoRR, 2022

Bayesian Inference with Latent Hamiltonian Neural Networks.

Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields

CoRR, 2022

Deep transfer learning for partial differential equations under conditional shift with DeepONet.

Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis

CoRR, 2022

2021

Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models.

Katiana Kontolati, Dimitrios Loukrezis, Ketson R. M. dos Santos, Dimitrios G. Giovanis, Michael D. Shields

CoRR, 2021

2020

UQpy: A general purpose Python package and development environment for uncertainty quantification.

Audrey Olivier, Dimitris G. Giovanis, B. S. Aakash, Mohit Chauhan, Lohit Vandanapu, Michael D. Shields

J. Comput. Sci., 2020

On the quantification and efficient propagation of imprecise probabilities with copula dependence.

Jiaxin Zhang, Michael D. Shields

Int. J. Approx. Reason., 2020

Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold.

Dimitris G. Giovanis, Michael D. Shields

CoRR, 2020

2019

Budgeting, Psychological Contracts, and Budgetary Misreporting.

Susanna Gallani, Ranjani Krishnan, Eric J. Marinich, Michael D. Shields

Manag. Sci., 2019

Uncertainty quantification (UQ) as an archetype for research: Integrating UQ into undergraduate research education.

B. S. Aakash, D. Perez-Roldan, A. Ibrahim, Michael D. Shields

Proceedings of the IEEE Frontiers in Education Conference, 2019

2018

Adaptive Monte Carlo analysis for strongly nonlinear stochastic systems.

Michael D. Shields

Reliab. Eng. Syst. Saf., 2018

Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations.

Dimitris G. Giovanis, Michael D. Shields

J. Comput. Phys., 2018

Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis.

Anindya Bhaduri, Yanyan He, Michael D. Shields, Lori Graham-Brady, Robert M. Kirby

J. Comput. Phys., 2018

2016

The generalization of Latin hypercube sampling.

Michael D. Shields, Jiaxin Zhang

Reliab. Eng. Syst. Saf., 2016

2015

Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification.

Michael D. Shields, Kirubel Teferra, Adam Hapij, Raymond P. Daddazio

Reliab. Eng. Syst. Saf., 2015

2014

Mapping model validation metrics to subject matter expert scores for model adequacy assessment.

Kirubel Teferra, Michael D. Shields, Adam Hapij, Raymond P. Daddazio

Reliab. Eng. Syst. Saf., 2014