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Publication	Type	Year
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
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
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
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
Optimal Experimental Design for Prediction Based on Push-forward Probability Measures Journal of Computational Physics Timothy Michael Wildey, Troy Butler, John Davis Jakeman https://www.osti.gov/search/identifier:1630281	Journal Article – 2020 Journal Article	2020
Impact of a minority relativistic electron tail interacting with a thermal plasma containing high-atomic-number impurities Physics Review Letters Timothy Michael Wildey, Nathan Garland, Hyun-Kyung Chung, Christopher Fontes, Mark Zammit, James Colgan, Todd Elder, Christopher McDevitt, Xian-Zhu Tang	Journal Article – 2020 Journal Article	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
Multiscale stochastic reduced-order model for uncertainty propagation using Fokker-Planck equation with microstructure evolution applications arXiv preprint Anh Tran, Jing Sun, Yan Wang, Dehao Liu, Timothy Michael Wildey https://www.osti.gov/search/identifier:1834331	Journal Article – 2020 Journal Article	2020
aphBO-2GP-3B: A budgeted asynchronously-parallel multi-acquisition for known/unknown constrained Bayesian optimization on high-performing computing architecture arXiv Anh Tran, Timothy Michael Wildey, John Furlan, Pagalthivarthi Krishnan, Robert Visintainer, Scott McCann https://www.osti.gov/search/identifier:1834333	Journal Article – 2020 Journal Article	2020
Data-consistent Inversion For Stochastic Input-to-output Maps Inverse Problems Timothy Michael Wildey, Troy Butler, Tian Yu Yen https://www.osti.gov/search/identifier:1619228	Journal Article – 2020 Journal Article	2020
sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications ASME Journal of Computing and Information Science in Engineering Anh Tran, Timothy Michael Wildey, Scott McCann https://www.osti.gov/search/identifier:1605731	Journal Article – 2020 Journal Article	2020
Convergence of Probability Densities using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification: Extensions to Lp SIAM/ASA Journal on Uncertainty Quantification Timothy Michael Wildey, Troy Butler, Wenjuan Zhang https://www.osti.gov/search/identifier:1595429	Journal Article – 2020 Journal Article	2020
Materials informatics: data-driven, materials design, and uncertainty quantification perspectives SNL 13th annual postdoc technical showcase Anh Tran, Timothy Michael Wildey https://www.osti.gov/search/identifier:1643479	Conference Paper – 2019 Conference Paper	2019
Moving Beyond Forward Simulation to Enable Data-informed Physics-based Predictions Colloquium at University of Colorado - Denver Timothy Michael Wildey, Lukas Bruder, Tan Bui-Thanh, Troy Butler, John Davis Jakeman, Brad Marvin, Anh Tran, Scott Walsh https://www.osti.gov/search/identifier:1646273	Presentation (non-conference) – 2019 Presentation (non-conference)	2019
Convergence of Probability Densities using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification AMS Fall Southeastern Sectional Meeting Timothy Michael Wildey, Troy Butler, John Davis Jakeman https://www.osti.gov/search/identifier:1641989	Conference Paper – 2019 Conference Paper	2019
sBF-BO-2CoGP: A sequential bi-fidelity constrained Bayesian optimization for design applications International Design Engineering Technical Conferences & Computers and Information in Engineering Conference Anh Tran, Timothy Michael Wildey, Scott McCann https://www.osti.gov/search/identifier:1641650	Conference Paper – 2019 Conference Paper	2019
A sequential bi-fidelity constrained Bayesian optimization for design applications ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference Anh Tran, Timothy Michael Wildey, Scott McCann https://www.osti.gov/search/identifier:1641565	Conference Paper – 2019 Conference Paper	2019
Solving Stochastic Inverse Problems using Approximate Push-forward Densities based on a Multi-fidelity Monte Carlo Method 9th International Congress on Industrial and Applied Mathematics Timothy Michael Wildey, Troy Butler, John Davis Jakeman, Lukas Bruder https://www.osti.gov/search/identifier:1641047	Conference Paper – 2019 Conference Paper	2019
Using High Performance Computing to Enable Data-informed Multiscale Modeling with Applications to Additive Materials Engineering Mechanics Institute Conference Timothy Michael Wildey, Anh Tran https://www.osti.gov/search/identifier:1640837	Conference Paper – 2019 Conference Paper	2019
A step towards a versatile Bayesian optimization: constrained, asynchronous batch-parallel, multi-fidelity, and mixed-integer extensions MUMS Transition Workshop and SPUQ Anh Tran, Yan Wang, Timothy Michael Wildey https://www.osti.gov/search/identifier:1640079	Conference Paper – 2019 Conference Paper	2019
Simultaneous Inversion of Shear Modulus and Traction Boundary Conditions in Biomechanical Imaging Inverse Problems in Science and Engineering Daniel Thomas Seidl, Bart G van Bloemen Waanders, Timothy Michael Wildey https://www.osti.gov/search/identifier:1515211	Journal Article – 2019 Journal Article	2019
Document Title	Type	Year

Results 26–50 of 144

Publications

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

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

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

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

Optimal Experimental Design for Prediction Based on Push-forward Probability Measures

Impact of a minority relativistic electron tail interacting with a thermal plasma containing high-atomic-number impurities

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

Multiscale stochastic reduced-order model for uncertainty propagation using Fokker-Planck equation with microstructure evolution applications

aphBO-2GP-3B: A budgeted asynchronously-parallel multi-acquisition for known/unknown constrained Bayesian optimization on high-performing computing architecture

Data-consistent Inversion For Stochastic Input-to-output Maps

sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications

Convergence of Probability Densities using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification: Extensions to Lp

Materials informatics: data-driven, materials design, and uncertainty quantification perspectives

Moving Beyond Forward Simulation to Enable Data-informed Physics-based Predictions

Convergence of Probability Densities using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification

sBF-BO-2CoGP: A sequential bi-fidelity constrained Bayesian optimization for design applications

A sequential bi-fidelity constrained Bayesian optimization for design applications

Solving Stochastic Inverse Problems using Approximate Push-forward Densities based on a Multi-fidelity Monte Carlo Method

Using High Performance Computing to Enable Data-informed Multiscale Modeling with Applications to Additive Materials

A step towards a versatile Bayesian optimization: constrained, asynchronous batch-parallel, multi-fidelity, and mixed-integer extensions

Simultaneous Inversion of Shear Modulus and Traction Boundary Conditions in Biomechanical Imaging