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Publication	Type	Year
Microstructure-Sensitive UQ for Materials Constitutive Models in Crystal Plasticity Finite Element Method USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (UQ-MLIP) Anh Tran, Timothy Michael Wildey, Hojun Lim	Conference Poster – 2022 Conference Poster	2022
Synchronous and Asynchronous Time Integration for Multiscale Simulations Using Hybridized Finite Element Methods 15th World Congress on Computational Mechanics Bryan William Reuter, Timothy Michael Wildey	Conference Presentation – 2022 Conference Presentation	2022
Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data Wccm-apcom 2022 Tian Yu NMN Yen, Timothy Michael Wildey	Conference Presentation – 2022 Conference Presentation	2022
Embedded uncertainty estimation for data-driven surrogates to enable trustworthy ML for UQ Machine Learning and Deep Learning Conference Timothy Michael Wildey, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, Owen Davis, Teresa Portone, Tian Yu NMN Yen, Bryan William Reuter, Alex Gorodetsky, Ahmad Rushdi, Daniele Schiavazzi, Lauren Partin	Conference Presentation – 2022 Conference Presentation	2022
Microstructure-sensitive uncertainty quantification for crystal plasticity finite element constitutive models using stochastic collocation methods Frontiers in Materials Anh Tran, Timothy Michael Wildey, Hojun Lim	Journal Article – 2022 Journal Article	2022
Multifidelity Uncertainty Quantification For Non-Deterministic Models ECCOMAS Congress 2022 Bryan William Reuter, Gianluca Geraci, Timothy Michael Wildey, Michael S. Eldred	Conference Presentation – 2022 Conference Presentation	2022
Multifaceted uncertainty quantification in crystal plasticity finite element model AIAA Science and Technology Forum and Exposition Anh Tran, Pieterjan Magda M Robbe, Timothy Michael Wildey, Guy Leshem Bergel, David Montes de Oca Zapiain, Hojun Lim	Abstract – 2022 Abstract	2022
Efficient Multifidelity Strategies for Uncertainty Quantification of Non-Deterministic Models Siam Uq 2022 Bryan William Reuter, Gianluca Geraci, Timothy Michael Wildey	Conference Presentation – 2022 Conference Presentation	2022
A Probabilistic Characterization of Aleatoric and Epistemic Uncertainty in Solutions to Stochastic Inverse Problems Using Machine Learning Surrogate Models SIAM Conference on Uncertainty Quantification Timothy Michael Wildey, Troy Butler, Tian Yu NMN Yen	Conference Presentation – 2022 Conference Presentation	2022
Quantifying Aleatoric and Epistemic Uncertainties in RLC Circuits with Data-consistent Inversion SIAM Conference on Uncertainty Quantification Tian Yu NMN Yen, Timothy Michael Wildey, Troy Butler	Conference Poster – 2022 Conference Poster	2022
Solving inverse problems in process-structure-property linkage with Gaussian process regression TMS World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022) Anh Tran, Timothy Michael Wildey	Conference Presentation – 2022 Conference Presentation	2022
A stochastic reduced-order model for statistical microstructure descriptors evolution ASME Journal of Computing and Information Science in Engineering Anh Tran, Jing Sun, Dehao Liu, Yan Wang, Timothy Michael Wildey	Journal Article – 2022 Journal Article	2022
Efficient Implicit-Explicit Time Integration for Multiscale Simulations Using Hybridized Finite Element Methods 15th World Congress on Computational Mechanics Bryan William Reuter, Timothy Michael Wildey	Abstract – 2022 Abstract	2022
Multifidelity Uncertainty Quantification For Non-Deterministic Models ECCOMAS Congress 2022 Bryan William Reuter, Gianluca Geraci, Timothy Michael Wildey, Michael S. Eldred	Abstract – 2021 Abstract	2021
Adaptive Concurrent Multiscale Modelling Using Hybridized Discretizations, Error Estimation and Machine Learning 15th World Congress on Computational Mechanics Timothy Michael Wildey	Abstract – 2021 Abstract	2021
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture Structural and Multidisciplinary Optimization Anh Tran, Michael S. Eldred, Timothy Michael Wildey, Scott McCann, Jing Sun, Robert Visintainer	Journal Article – 2021 Journal Article	2021
Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data 15th World Congress on Computational Mechanics Tian Yu NMN Yen, Timothy Michael Wildey	Abstract – 2021 Abstract	2021
Efficient Multifidelity Strategies for Uncertainty Quantification of Non-Deterministic Models SIAM Conference on Uncertainty Quantification (UQ22) Bryan William Reuter, Gianluca Geraci, Timothy Michael Wildey	Abstract – 2021 Abstract	2021
The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact NASA Langley HPC Seminar Series Michael S. Eldred, Gianluca Geraci, Alex Arkady Gorodetsky, John Davis Jakeman, Teresa Portone, Timothy Michael Wildey, Ahmad Rushdi, Daniel Thomas Seidl	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Solving Stochastic Inverse Problems for Property-Structure Relationships in Computational Materials Science US National Congress on Computational Mechanics Timothy Michael Wildey, Troy Butler, John Davis Jakeman, Anh Tran	Conference Presentation – 2021 Conference Presentation	2021
Combining Measure Theory and Bayes? Rule to Solve a Stochastic Inverse Problem Emi/pmc 2021 Timothy Michael Wildey, Troy Butler, John Davis Jakeman https://www.osti.gov/search/identifier:1877851	Conference Presentation – 2021 Conference Presentation	2021
Solving inverse problems for process-structure linkages using asynchronous parallel Bayesian optimization TMS 2021 Annual Meeting & Exhibition Anh Tran, Timothy Michael Wildey https://www.osti.gov/search/identifier:1854075	Conference Presentation – 2021 Conference Presentation	2021
Multi-fidelity ML/UQ and Bayesian Optimization for Materials Design: Application to Ternary Random Alloys TMS 2021 Annual Meeting & Exhibition (TMS2021) Anh Tran, Julien Guy Tranchida, Timothy Michael Wildey, Aidan P. Thompson https://www.osti.gov/search/identifier:1853874	Conference Poster – 2021 Conference Poster	2021
Solving stochastic inverse problems for property-structure linkage using data-consistent inversion and ML TMS 2021 Annual Meeting & Exhibition Anh Tran, Timothy Michael Wildey https://www.osti.gov/search/identifier:1848050	Conference Poster – 2021 Conference Poster	2021
Data-consistent Solutions To Stochastic Inverse Problems Using A Probabilistic Multi-fidelity Method Based On Conditional Densities International Journal for Uncertainty Quantification Timothy Michael Wildey, Lukas Bruder, Michael Gee https://www.osti.gov/search/identifier:1769923	Journal Article – 2021 Journal Article	2021
Document Title	Type	Year

Results 1–25 of 144

Publications

Microstructure-Sensitive UQ for Materials Constitutive Models in Crystal Plasticity Finite Element Method

Synchronous and Asynchronous Time Integration for Multiscale Simulations Using Hybridized Finite Element Methods

Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data

Embedded uncertainty estimation for data-driven surrogates to enable trustworthy ML for UQ

Microstructure-sensitive uncertainty quantification for crystal plasticity finite element constitutive models using stochastic collocation methods

Multifidelity Uncertainty Quantification For Non-Deterministic Models

Multifaceted uncertainty quantification in crystal plasticity finite element model

Efficient Multifidelity Strategies for Uncertainty Quantification of Non-Deterministic Models

A Probabilistic Characterization of Aleatoric and Epistemic Uncertainty in Solutions to Stochastic Inverse Problems Using Machine Learning Surrogate Models

Quantifying Aleatoric and Epistemic Uncertainties in RLC Circuits with Data-consistent Inversion

Solving inverse problems in process-structure-property linkage with Gaussian process regression

A stochastic reduced-order model for statistical microstructure descriptors evolution

Efficient Implicit-Explicit Time Integration for Multiscale Simulations Using Hybridized Finite Element Methods

Multifidelity Uncertainty Quantification For Non-Deterministic Models

Adaptive Concurrent Multiscale Modelling Using Hybridized Discretizations, Error Estimation and Machine Learning

aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture

Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data

Efficient Multifidelity Strategies for Uncertainty Quantification of Non-Deterministic Models

The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact

Solving Stochastic Inverse Problems for Property-Structure Relationships in Computational Materials Science

Combining Measure Theory and Bayes? Rule to Solve a Stochastic Inverse Problem

Solving inverse problems for process-structure linkages using asynchronous parallel Bayesian optimization

Multi-fidelity ML/UQ and Bayesian Optimization for Materials Design: Application to Ternary Random Alloys

Solving stochastic inverse problems for property-structure linkage using data-consistent inversion and ML

Data-consistent Solutions To Stochastic Inverse Problems Using A Probabilistic Multi-fidelity Method Based On Conditional Densities