Michael D. Shields

56 papers C 1Journal 55

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Physics-Informed Gaussian Process Regression for the Constitutive Modeling of Concrete: A Data-Driven Improvement to Phenomenological Models. CoRR Chenyang Li, Himanshu Sharma, Youcai Wu, Joseph Magallanes, K. T. Ramesh, Michael D. Shields
2026	J	`jnl`	Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves. CoRR George D. Pasparakis, Himanshu Sharma, Rushik Desai, Chunyu Li, Alejandro Strachan, Lori Graham-Brady, Michael D. Shields
2025	J	`jnl`	A Physics-informed Multi-resolution Neural Operator. CoRR Sumanta Roy, Bahador Bahmani, Ioannis G. Kevrekidis, Michael D. Shields
2025	J	`jnl`	Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators. CoRR Ponkrshnan Thiagarajan, Tamer A. Zaki, Michael D. Shields
2025	J	`jnl`	Covariance-Free Bifidelity Control Variates Importance Sampling for Rare Event Reliability Analysis. SIAM/ASA J. Uncertain. Quantification Promit Chakroborty, Somayajulu L. N. Dhulipala, Michael D. Shields
2025	J	`jnl`	Joint space-time wind field data extrapolation and uncertainty quantification using nonparametric Bayesian dictionary learning. CoRR George D. Pasparakis, Ioannis A. Kougioumtzoglou, Michael D. Shields
2025	J	`jnl`	Neural Chaos: A Spectral Stochastic Neural Operator. CoRR Bahador Bahmani, Ioannis G. Kevrekidis, Michael D. Shields
2025	J	`jnl`	Neural Operators for Stochastic Modeling of Nonlinear Structural System Response to Natural Hazards. CoRR Somdatta Goswami, Dimitris G. Giovanis, Bowei Li, Seymour M. J. Spence, Michael D. Shields
2025	J	`jnl`	Neural chaos: A spectral stochastic neural operator. J. Comput. Phys. Bahador Bahmani, Ioannis G. Kevrekidis, Michael D. Shields
2025	J	`jnl`	Physics-informed Polynomial Chaos Expansion with Enhanced Constrained Optimization Solver and D-optimal Sampling. CoRR Qitian Lu, Himanshu Sharma, Michael D. Shields, Lukás Novák
2025	J	`jnl`	Polynomial Chaos Expansion for Operator Learning. CoRR Himanshu Sharma, Lukás Novák, Michael D. Shields
2025	J	`jnl`	Synergistic learning with multi-task DeepONet for efficient PDE problem solving. Neural Networks Varun Kumar, Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis
2025	J	`jnl`	UQpy Version 4.2: Uncertainty quantification with Python. SoftwareX Connor Krill, Ponkrshnan Thiagarajan, George D. Pasparakis, Somdatta Goswami, Dimitrios Tsapetis, Dimitris G. Giovanis, Michael D. Shields
2024	J	`jnl`	A Resolution Independent Neural Operator. CoRR Bahador Bahmani, Somdatta Goswami, Ioannis G. Kevrekidis, Michael D. Shields
2024	J	`jnl`	Bayesian neural networks for predicting uncertainty in full-field material response. CoRR George D. Pasparakis, Lori Graham-Brady, Michael D. Shields
2024	J	`jnl`	On active learning for Gaussian process-based global sensitivity analysis. Reliab. Eng. Syst. Saf. Mohit Singh Chauhan, Mariel Ojeda-Tuz, Ryan Catarelli, Kurtis R. Gurley, Dimitrios Tsapetis, Michael D. Shields
2024	J	`jnl`	Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification. CoRR Himanshu Sharma, Lukás Novák, Michael D. Shields
2024	J	`jnl`	Physics-informed polynomial chaos expansions. J. Comput. Phys. Lukás Novák, Himanshu Sharma, Michael D. Shields
2024	J	`jnl`	Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification. CoRR Dimitris G. Giovanis, Dimitrios Loukrezis, Ioannis G. Kevrekidis, Michael D. Shields
2024	J	`jnl`	Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification. J. Comput. Phys. Dimitris G. Giovanis, Dimitrios Loukrezis, Ioannis G. Kevrekidis, Michael D. Shields
2024	J	`jnl`	Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks. CoRR Denny Thaler, Somayajulu L. N. Dhulipala, Franz Bamer, Bernd Markert, Michael D. Shields
2024	J	`jnl`	Simulation of non-Gaussian wind field as a 3rd-order stochastic wave. Reliab. Eng. Syst. Saf. Lohit Vandanapu, Michael D. Shields
2024	J	`jnl`	Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving. CoRR Varun Kumar, Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis
2023	J	`jnl`	Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion. CoRR Lukás Novák, Michael D. Shields, Václav Sadílek, Miroslav Vorechovský
2023	J	`jnl`	Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling. J. Comput. Phys. Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields
2023	J	`jnl`	Learning in latent spaces improves the predictive accuracy of deep neural operators. CoRR Katiana Kontolati, Somdatta Goswami, George Em Karniadakis, Michael D. Shields
2023	J	`jnl`	On Active Learning for Gaussian Process-based Global Sensitivity Analysis. CoRR Mohit Singh Chauhan, Mariel Ojeda-Tuz, Ryan Catarelli, Kurtis R. Gurley, Dimitrios Tsapetis, Michael D. Shields
2023	J	`jnl`	On the influence of over-parameterization in manifold based surrogates and deep neural operators. J. Comput. Phys. Katiana Kontolati, Somdatta Goswami, Michael D. Shields, George Em Karniadakis
2023	J	`jnl`	Physics-Informed Polynomial Chaos Expansions. CoRR Lukás Novák, Himanshu Sharma, Michael D. Shields
2023	J	`jnl`	UQpy v4.1: Uncertainty Quantification with Python. CoRR Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Lukás Novák, Promit Chakroborty, Himanshu Sharma, Mohit Singh Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner
2023	J	`jnl`	UQpy v4.1: Uncertainty quantification with Python. SoftwareX Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Lukás Novák, Promit Chakroborty, Himanshu Sharma, Mohit Singh Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner
2022	J	`jnl`	A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems. CoRR Katiana Kontolati, Dimitrios Loukrezis, Dimitrios G. Giovanis, Lohit Vandanapu, Michael D. Shields
2022	J	`jnl`	A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems. J. Comput. Phys. Katiana Kontolati, Dimitrios Loukrezis, Dimitrios G. Giovanis, Lohit Vandanapu, Michael D. Shields
2022	J	`jnl`	Active learning with multifidelity modeling for efficient rare event simulation. J. Comput. Phys. Somayajulu L. N. Dhulipala, Michael D. Shields, Benjamin W. Spencer, Chandrakanth Bolisetti, Andrew E. Slaughter, Vincent M. Laboure, Promit Chakroborty
2022	J	`jnl`	Bayesian Inference with Latent Hamiltonian Neural Networks. CoRR Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields
2022	J	`jnl`	Deep transfer learning for partial differential equations under conditional shift with DeepONet. CoRR Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis
2022	J	`jnl`	Deep transfer operator learning for partial differential equations under conditional shift. Nat. Mac. Intell. Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis
2022	J	`jnl`	General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation. CoRR Promit Chakroborty, Somayajulu L. N. Dhulipala, Yifeng Che, Wen Jiang, Benjamin W. Spencer, Jason D. Hales, Michael D. Shields
2022	J	`jnl`	Grassmannian Diffusion Maps-Based Dimension Reduction and Classification for High-Dimensional Data. SIAM J. Sci. Comput. Ketson R. M. dos Santos, Dimitrios G. Giovanis, Michael D. Shields
2022	J	`jnl`	On the influence of over-parameterization in manifold based surrogates and deep neural operators. CoRR Katiana Kontolati, Somdatta Goswami, Michael D. Shields, George Em Karniadakis
2022	J	`jnl`	Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference. CoRR Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields
2022	J	`jnl`	Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation. Reliab. Eng. Syst. Saf. 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
2021	J	`jnl`	Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation. CoRR Somayajulu L. N. Dhulipala, Michael D. Shields, Benjamin W. Spencer, Chandrakanth Bolisetti, Andrew E. Slaughter, Vincent M. Laboure, Promit Chakroborty
2021	J	`jnl`	Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models. CoRR Katiana Kontolati, Dimitrios Loukrezis, Ketson R. M. dos Santos, Dimitrios G. Giovanis, Michael D. Shields
2020	J	`jnl`	Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold. CoRR Dimitris G. Giovanis, Michael D. Shields
2020	J	`jnl`	Grassmannian diffusion maps based dimension reduction and classification for high-dimensional data. CoRR K. R. M. dos Santos, Dimitris G. Giovanis, Michael D. Shields
2020	J	`jnl`	On the quantification and efficient propagation of imprecise probabilities with copula dependence. Int. J. Approx. Reason. Jiaxin Zhang, Michael D. Shields
2020	J	`jnl`	UQpy: A general purpose Python package and development environment for uncertainty quantification. J. Comput. Sci. Audrey Olivier, Dimitris G. Giovanis, B. S. Aakash, Mohit Singh Chauhan, Lohit Vandanapu, Michael D. Shields
2019	J	`jnl`	Budgeting, Psychological Contracts, and Budgetary Misreporting. Manag. Sci. Susanna Gallani, Ranjani Krishnan, Eric J. Marinich, Michael D. Shields
2019	C	`conf`	Uncertainty quantification (UQ) as an archetype for research: Integrating UQ into undergraduate research education. FIE B. S. Aakash, D. Perez-Roldan, A. Ibrahim, Michael D. Shields
2018	J	`jnl`	Adaptive Monte Carlo analysis for strongly nonlinear stochastic systems. Reliab. Eng. Syst. Saf. Michael D. Shields
2018	J	`jnl`	Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis. J. Comput. Phys. Anindya Bhaduri, Yanyan He, Michael D. Shields, Lori Graham-Brady, Robert M. Kirby
2018	J	`jnl`	Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations. J. Comput. Phys. Dimitris G. Giovanis, Michael D. Shields
2016	J	`jnl`	The generalization of Latin hypercube sampling. Reliab. Eng. Syst. Saf. Michael D. Shields, Jiaxin Zhang
2015	J	`jnl`	Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification. Reliab. Eng. Syst. Saf. Michael D. Shields, Kirubel Teferra, Adam Hapij, Raymond P. Daddazio
2014	J	`jnl`	Mapping model validation metrics to subject matter expert scores for model adequacy assessment. Reliab. Eng. Syst. Saf. Kirubel Teferra, Michael D. Shields, Adam Hapij, Raymond P. Daddazio