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Publication	Type	Year
Multi-Output Surrogate Construction for Fusion Simulations 63rd Annual Meeting of the APS Division of Plasma Physics Kathryn Anne Maupin, Anh Tran, Michael Edwin Glinsky, Patrick Knapp, William Lewis	Abstract – 2021 Abstract	2021
Multi-Output Surrogate Construction for Fusion Simulations US National Congress on Computational Mechanics Kathryn Anne Maupin, Anh Tran, Michael Edwin Glinsky	Conference Presentation – 2021 Conference Presentation	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
Scalable3-BO: Big Data meets HPC - A scalable parallel high-dimensional Bayesian optimization framework on supercomputers Asme Idetc/cie 2021 Anh Tran https://www.osti.gov/search/identifier:1875390	Conference Presentation – 2021 Conference Presentation	2021
Scalable3-BO: Big Data meets HPC - A scalable asynchronous parallel high-dimensional Bayesian optimization framework on supercomputers ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference Anh Tran https://www.osti.gov/search/identifier:1870365	Conference Paper – 2021 Conference Paper	2021
Gaussian processes - UQ and optimization for multiscale computational materials design GE Research Seminar Anh Tran	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Microstructure reconstruction via non-local patch-based image inpainting TMS 2021 Annual Meeting & Exhibition Anh Tran, Hoang Tran https://www.osti.gov/search/identifier:1854430	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
Multi-Output Surrogate Construction for Fusion Simulations 16th U.S. National Congress on Computational Mechanics Kathryn Anne Maupin, Anh Tran	Abstract – 2021 Abstract	2021
Solving stochastic inverse problems for property-structure linkages using data-consistent inversion and machine learning Jom Anh Tran, Timothy Michael Wildey https://www.osti.gov/search/identifier:1760459	Journal Article – 2020 Journal Article	2020
Solving stochastic inverse problems for structure-property linkages using data-consistent inversion TMS 2021 Annual Meeting & Exhibition Anh Tran, Timothy Michael Wildey https://www.osti.gov/search/identifier:1825594	Conference Paper – 2020 Conference Paper	2020
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:1825595	Conference Paper – 2020 Conference Paper	2020
2D microstructure reconstruction for SEM via non-local patch-based image inpainting TMS 2021 Annual Meeting & Exhibition Anh Tran, Hoang Tran https://www.osti.gov/search/identifier:1825596	Conference Paper – 2020 Conference Paper	2020
Reification of latent microstructures: On supervised, unsupervised, and semi-supervised deep learning applications for microstructures in materials informatics Anh Tran, Theron Rodgers, Timothy Michael Wildey https://www.osti.gov/search/identifier:1673174	SAND Report – 2020 SAND Report	2020
Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys The Journal of Chemical Physics Julien Guy Tranchida, Anh Tran, Timothy Michael Wildey, Aidan P. Thompson	Journal Article – 2020 Journal Article	2020
On supervised and unsupervised deep learning applications for materials informatics Sandia Machine Learning and Deep Learning Workshop Anh Tran, Timothy Michael Wildey, Theron Rodgers https://www.osti.gov/search/identifier:1812467	Conference Paper – 2020 Conference Paper	2020
srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference Anh Tran, Michael S. Eldred, Yan Wang, Scott McCann https://www.osti.gov/search/identifier:1811793	Conference Paper – 2020 Conference Paper	2020
Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys Sandia MLDL Workshop Julien Guy Tranchida, Aidan P. Thompson, Anh Tran, Timothy Michael Wildey	Abstract – 2020 Abstract	2020
srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference Anh Tran, Michael S. Eldred, Scott McCann, Yan Wang https://www.osti.gov/search/identifier:1808255	Conference Paper – 2020 Conference Paper	2020
WearGP: A UQ/ML wear prediction framework for slurry impellers and casings ASME Fluids Engineering Division Summer Meeting 2020 Anh Tran, Robert Visintainer, John Furlan, Krishnan Pagalthivarthi, Mohamed Garman, Aaron Cutright, Yan Wang https://www.osti.gov/search/identifier:1808256	Conference Paper – 2020 Conference Paper	2020
Multi-fidelity machine-learning with uncertainty quantification for materials design: Application to ternary random alloys arXiv Anh Tran, Timothy Michael Wildey, Julien Guy Tranchida, Aidan P. Thompson https://www.osti.gov/search/identifier:1670182	Journal Article – 2020 Journal Article	2020
Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 Theory Manual Keith Dalbey, Michael S. Eldred, Gianluca Geraci, John Davis Jakeman, Kathryn Anne Maupin, Jason A. Monschke, Daniel Thomas Seidl, Laura Painton Swiler, Anh Tran, Friedrich Menhorn, Xiaoshu Zeng https://www.osti.gov/search/identifier:1630693	SAND Report – 2020 SAND Report	2020
An active learning high-throughput microstructure calibration framework for solving inverse structure-process problems in materials informatics Acta Materialia Anh Tran, John A. Mitchell, Laura Painton Swiler, Timothy Michael Wildey https://www.osti.gov/search/identifier:1634790	Journal Article – 2020 Journal Article	2020
Document Title	Type	Year

Results 26–50 of 66

Publications

Multi-Output Surrogate Construction for Fusion Simulations

Multi-Output Surrogate Construction for Fusion Simulations

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

Scalable3-BO: Big Data meets HPC - A scalable parallel high-dimensional Bayesian optimization framework on supercomputers

Scalable3-BO: Big Data meets HPC - A scalable asynchronous parallel high-dimensional Bayesian optimization framework on supercomputers

Gaussian processes - UQ and optimization for multiscale computational materials design

Microstructure reconstruction via non-local patch-based image inpainting

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

Multi-Output Surrogate Construction for Fusion Simulations

Solving stochastic inverse problems for property-structure linkages using data-consistent inversion and machine learning

Solving stochastic inverse problems for structure-property linkages using data-consistent inversion

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

2D microstructure reconstruction for SEM via non-local patch-based image inpainting

Reification of latent microstructures: On supervised, unsupervised, and semi-supervised deep learning applications for microstructures in materials informatics

Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys

On supervised and unsupervised deep learning applications for materials informatics

srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications

Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys

srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications

WearGP: A UQ/ML wear prediction framework for slurry impellers and casings

Multi-fidelity machine-learning with uncertainty quantification for materials design: Application to ternary random alloys

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 Theory Manual

An active learning high-throughput microstructure calibration framework for solving inverse structure-process problems in materials informatics