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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
Multi-task Machine Learning for Fusion Simulations USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling William Lewis, Kathryn Anne Maupin, Anh Tran, Patrick Knapp	Conference Poster – 2022 Conference Poster	2022
Development of structure-property linkages for damage in crystalline microstructures using Bayesian inference and unsupervised learning Tms 2023 David Montes de Oca Zapiain, Anh Tran, Hojun Lim	Abstract – 2022 Abstract	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
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
Beyond forward ICME models: A perspective on materials design as inverse problems Data-driven Modeling and Optimization along the Processing-Structure-Properties Chain for Materials and Process Design Hybrid Workshop Anh Tran	Conference Presentation – 2022 Conference Presentation	2022
Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.16 User's Manual Brian Homer Adams, William (SNL) Bohnhoff, Keith (SNL) Dalbey, Mohamed (SNL) Ebeida, John (SNL) Eddy, Michael (SNL) Eldred, Russell (SNL) Hooper, Patricia (SNL) Hough, Kenneth (SNL) Hu, John (SNL) Jakeman, Mohammad (SNL) Khalil, Kathryn (SNL) Maupin, Jason A. Monschke, Elliott (SNL) Ridgway, Ahmad (SNL) Rushdi, Daniel (SNL) Seidl, John (SNL) Stephens, Laura Painton Swiler, Anh Tran, Justin (SNL) Winokur https://www.osti.gov/search/identifier:1868142	SAND Report – 2022 SAND Report	2022
Advanced Gaussian process regression and optimization for computational materials science applications SIAM Conference on Uncertainty Quantification 2022 Anh Tran	Conference Presentation – 2022 Conference Presentation	2022
Multi-Output Surrogate Construction for Fusion Simulations SIAM Conference on Uncertainty Quantification Kathryn Anne Maupin, Anh Tran, Michael Edwin Glinsky, William Lewis	Conference Presentation – 2022 Conference Presentation	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
Versatile Gaussian process and Bayesian optimization for multiscale computational materials science applications Mathematics in Computation (MiC) seminar series Anh Tran	Conference Presentation – 2022 Conference Presentation	2022
Enabling the fourth paradigm of multiscale ICME models through versatile Gaussian process and Bayesian optimization Materials Science & Technology (MS&T) 2022 Anh Tran	Abstract – 2022 Abstract	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
Versatile Gaussian process and Bayesian optimization for multiscale computational materials science applications Mathematics in Computation (MiC) seminar series - Oak Ridge National Laboratory Anh Tran	Abstract – 2022 Abstract	2022
Mesoscale Simulation of Cold Spray Microstructure Formation TMS 2022 Annual Meeting & Exhibition Theron Rodgers, David Montes de Oca Zapiain, Anh Tran, Shannon Elizabeth Murray, Jacob Thomas Mahaffey	Conference Presentation – 2022 Conference Presentation	2022
Integrated Computational Materials Engineering with Monotonic Gaussian Processes ASME 2022 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference Anh Tran, Kathryn Anne Maupin, Theron Rodgers	Conference Paper – 2022 Conference Paper	2022
Promoting Data Science in Mechanical Engineering Research and Education: An Exploration of the Hackathon Mechanism Emotional Engineering, Vol.9 Zhenghui Sha, Yan Lu, Christopher McComb, Zhuo Yang, Anh Tran, Dehao Liu, Faez Ahmed, Bingyang Song, Bryan O'Halloran, Brandon Lane, Anant Kumar Mishra, Ho Yeung, Michael C Lawton, Lucia Mirabella, Rahul Rai, Dazhong Wu	Book – 2021 Book	2021
Physics-Constrained Multi-Task Machine Learning for Fusion Simulations ECCOMAS Congress 2022 Anh Tran, Kathryn Anne Maupin, William Lewis, Michael Edwin Glinsky, Patrick Knapp	Abstract – 2021 Abstract	2021
Multi-fidelity Gaussian process and Bayesian optimization for materials design: Application to ternary random alloys Psi-k Workshop 2021Machine learning interatomic potentials - Young researchers's tutorial Anh Tran, Julien Tranchida	Conference Presentation – 2021 Conference Presentation	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
Multi-Output Surrogate Construction for Fusion Simulations SIAM Conference on Uncertainty Quantification Kathryn Anne Maupin, Anh Tran	Abstract – 2021 Abstract	2021
Gaussian process and Bayesian optimization - Bridging the gap between theory and practice in materials science SIAM Southeastern Atlantic Section Conference Anh Tran	Conference Presentation – 2021 Conference Presentation	2021
Physics-Informed and Data-Driven Predictive Models with Quantified Uncertainty SIAM Conference on Uncertainty Quantification in Atlanta Anh Tran, Kathryn Anne Maupin	Abstract – 2021 Abstract	2021
Quantifying Predictive Uncertainty with Physics-Informed Machine Learning SIAM Conference on Uncertainty Quantification in Atlanta Anh Tran, Kathryn Anne Maupin	Abstract – 2021 Abstract	2021
srMO-BO-3GP: A sequential regularized multi-objective Bayesian optimization for constrained design applications using an uncertain Pareto classifier ASME Journal of Mechanical Design Anh Tran, Michael S. Eldred, Scott McCann, Yan Wang https://www.osti.gov/search/identifier:1820417	Journal Article – 2021 Journal Article	2021
Document Title	Type	Year

Results 1–25 of 66

Publications

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

Multi-task Machine Learning for Fusion Simulations

Development of structure-property linkages for damage in crystalline microstructures using Bayesian inference and unsupervised learning

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

Multifaceted uncertainty quantification in crystal plasticity finite element model

Beyond forward ICME models: A perspective on materials design as inverse problems

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.16 User's Manual

Advanced Gaussian process regression and optimization for computational materials science applications

Multi-Output Surrogate Construction for Fusion Simulations

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

Versatile Gaussian process and Bayesian optimization for multiscale computational materials science applications

Enabling the fourth paradigm of multiscale ICME models through versatile Gaussian process and Bayesian optimization

A stochastic reduced-order model for statistical microstructure descriptors evolution

Versatile Gaussian process and Bayesian optimization for multiscale computational materials science applications

Mesoscale Simulation of Cold Spray Microstructure Formation

Integrated Computational Materials Engineering with Monotonic Gaussian Processes

Promoting Data Science in Mechanical Engineering Research and Education: An Exploration of the Hackathon Mechanism

Physics-Constrained Multi-Task Machine Learning for Fusion Simulations

Multi-fidelity Gaussian process and Bayesian optimization for materials design: Application to ternary random alloys

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

Multi-Output Surrogate Construction for Fusion Simulations

Gaussian process and Bayesian optimization - Bridging the gap between theory and practice in materials science

Physics-Informed and Data-Driven Predictive Models with Quantified Uncertainty

Quantifying Predictive Uncertainty with Physics-Informed Machine Learning

srMO-BO-3GP: A sequential regularized multi-objective Bayesian optimization for constrained design applications using an uncertain Pareto classifier