Michael D. Shields

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 40

Top Topics

High Dimensional

Bayesian Inference

Top Venues

J. Comput. Phys.

Reliab. Eng. Syst. Saf.

Publications

Dimitris G. Giovanis, Dimitrios Loukrezis, Ioannis G. Kevrekidis, Michael D. Shields
Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification. CoRR (2024)
Denny Thaler, Somayajulu L. N. Dhulipala, Franz Bamer, Bernd Markert, Michael D. Shields
Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks. CoRR (2024)
Himanshu Sharma, Lukás Novák, Michael D. Shields
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification. CoRR (2024)
George D. Pasparakis, Lori Graham-Brady, Michael D. Shields
Bayesian neural networks for predicting uncertainty in full-field material response. CoRR (2024)
Lukás Novák, Himanshu Sharma, Michael D. Shields
Physics-informed polynomial chaos expansions. J. Comput. Phys. 506 (2024)
Mohit Chauhan, Mariel Ojeda-Tuz, Ryan Catarelli, Kurtis R. Gurley, Dimitrios Tsapetis, Michael D. Shields
On active learning for Gaussian process-based global sensitivity analysis. Reliab. Eng. Syst. Saf. 245 (2024)
Lohit Vandanapu, Michael D. Shields
Simulation of non-Gaussian wind field as a 3rd-order stochastic wave. Reliab. Eng. Syst. Saf. 245 (2024)
Bahador Bahmani, Somdatta Goswami, Ioannis G. Kevrekidis, Michael D. Shields
A Resolution Independent Neural Operator. CoRR (2024)
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
UQpy v4.1: Uncertainty Quantification with Python. CoRR (2023)
Katiana Kontolati, Somdatta Goswami, Michael D. Shields, George Em Karniadakis
On the influence of over-parameterization in manifold based surrogates and deep neural operators. J. Comput. Phys. 479 (2023)
Katiana Kontolati, Somdatta Goswami, George Em Karniadakis, Michael D. Shields
Learning in latent spaces improves the predictive accuracy of deep neural operators. CoRR (2023)
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
UQpy v4.1: Uncertainty quantification with Python. SoftwareX 24 (2023)
Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields
Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling. J. Comput. Phys. 492 (2023)
Mohit Chauhan, Mariel Ojeda-Tuz, Ryan Catarelli, Kurtis Gurley, Dimitrios Tsapetis, Michael D. Shields
On Active Learning for Gaussian Process-based Global Sensitivity Analysis. CoRR (2023)
Lukás Novák, Himanshu Sharma, Michael D. Shields
Physics-Informed Polynomial Chaos Expansions. CoRR (2023)
Lukás Novák, Michael D. Shields, Václav Sadílek, Miroslav Vorechovský
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion. CoRR (2023)
Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis
Deep transfer operator learning for partial differential equations under conditional shift. Nat. Mac. Intell. 4 (12) (2022)
Katiana Kontolati, Somdatta Goswami, Michael D. Shields, George Em Karniadakis
On the influence of over-parameterization in manifold based surrogates and deep neural operators. CoRR (2022)
Somayajulu L. N. Dhulipala, Michael D. Shields, Benjamin W. Spencer, Chandrakanth Bolisetti, Andrew E. Slaughter, Vincent M. Laboure, Promit Chakroborty
Active learning with multifidelity modeling for efficient rare event simulation. J. Comput. Phys. 468 (2022)
Ketson R. M. dos Santos, Dimitrios G. Giovanis, Michael D. Shields
Grassmannian Diffusion Maps-Based Dimension Reduction and Classification for High-Dimensional Data. SIAM J. Sci. Comput. 44 (2) (2022)
Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields
Bayesian Inference with Latent Hamiltonian Neural Networks. CoRR (2022)
Promit Chakroborty, Somayajulu L. N. Dhulipala, Yifeng Che, Wen Jiang, Benjamin W. Spencer, Jason D. Hales, Michael D. Shields
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation. CoRR (2022)
Katiana Kontolati, Dimitrios Loukrezis, Dimitrios G. Giovanis, Lohit Vandanapu, Michael D. Shields
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems. CoRR (2022)
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
Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation. Reliab. Eng. Syst. Saf. 226 (2022)
Katiana Kontolati, Dimitrios Loukrezis, Dimitrios G. Giovanis, Lohit Vandanapu, Michael D. Shields
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems. J. Comput. Phys. 464 (2022)
Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis
Deep transfer learning for partial differential equations under conditional shift with DeepONet. CoRR (2022)
Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference. CoRR (2022)
Somayajulu L. N. Dhulipala, Michael D. Shields, Benjamin W. Spencer, Chandrakanth Bolisetti, Andrew E. Slaughter, Vincent M. Laboure, Promit Chakroborty
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation. CoRR (2021)
Katiana Kontolati, Dimitrios Loukrezis, Ketson R. M. dos Santos, Dimitrios G. Giovanis, Michael D. Shields
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models. CoRR (2021)
Dimitris G. Giovanis, Michael D. Shields
Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold. CoRR (2020)
Jiaxin Zhang, Michael D. Shields
On the quantification and efficient propagation of imprecise probabilities with copula dependence. Int. J. Approx. Reason. 122 (2020)
Audrey Olivier, Dimitris G. Giovanis, B. S. Aakash, Mohit Chauhan, Lohit Vandanapu, Michael D. Shields
UQpy: A general purpose Python package and development environment for uncertainty quantification. J. Comput. Sci. 47 (2020)
K. R. M. dos Santos, Dimitris G. Giovanis, Michael D. Shields
Grassmannian diffusion maps based dimension reduction and classification for high-dimensional data. CoRR (2020)
B. S. Aakash, D. Perez-Roldan, A. Ibrahim, Michael D. Shields
Uncertainty quantification (UQ) as an archetype for research: Integrating UQ into undergraduate research education. FIE (2019)
Susanna Gallani, Ranjani Krishnan, Eric J. Marinich, Michael D. Shields
Budgeting, Psychological Contracts, and Budgetary Misreporting. Manag. Sci. 65 (6) (2019)
Anindya Bhaduri, Yanyan He, Michael D. Shields, Lori Graham-Brady, Robert M. Kirby
Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis. J. Comput. Phys. 371 (2018)
Michael D. Shields
Adaptive Monte Carlo analysis for strongly nonlinear stochastic systems. Reliab. Eng. Syst. Saf. 175 (2018)
Dimitris G. Giovanis, Michael D. Shields
Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations. J. Comput. Phys. 364 (2018)
Michael D. Shields, Jiaxin Zhang
The generalization of Latin hypercube sampling. Reliab. Eng. Syst. Saf. 148 (2016)
Michael D. Shields, Kirubel Teferra, Adam Hapij, Raymond P. Daddazio
Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification. Reliab. Eng. Syst. Saf. 142 (2015)
Kirubel Teferra, Michael D. Shields, Adam Hapij, Raymond P. Daddazio
Mapping model validation metrics to subject matter expert scores for model adequacy assessment. Reliab. Eng. Syst. Saf. 132 (2014)