Anh Tran
Scientific Machine Learning
Scientific Machine Learning
Sandia National Laboratories, New Mexico
P.O. Box 5800
Albuquerque, NM 87185-1323
Biography
Anh Tran joined Sandia National Laboratories (Albuquerque, NM) as a postdoctoral appointee in January 2019 and became a staff member at the Center of Computing Research since September 2020. He has worked at the Uncertainty Quantification and Optimization department, as well as the Scientific Machine Learning department. His wide research interest include optimization, optimal experimental design, machine learning, and uncertainty quantification methodologies for multiscale computational materials science applications.
In the last few years, he has been focusing on Gaussian process regression and Bayesian optimization, as well as uncertainty quantification applications for materials science at multiple length-scales and time-scales for materials design. Some applications include phase-field simulation, kinetic Monte Carlo, molecular dynamics, and crystal plasticity finite element.
He is a member of ASME, TMS, SIAM, and USACM.
Education
Ph.D., Mechanical Engineering, Georgia Institute of Technology, Dec 2018.
M.S., Mechanical Engineering, Georgia Institute of Technology, May 2018.
M.S., Mathematics, Georgia Southern University, May 2014.
B.S., Mechanical Engineering, Georgia Institute of Technology, Dec 2011.
Publications
Anh Tran, Kathryn Maupin, Theron Rodgers, (2022). Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications Journal of Computing and Information Science in Engineering https://doi.org/10.1115/1.4055852 Publication ID: 80158
Anh Tran, Tim Wildey, Hojun Lim, (2022). Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods Frontiers in Materials https://doi.org/10.3389/fmats.2022.915254 Publication ID: 80842
Kathryn Maupin, Anh Tran, William Lewis, Patrick Knapp, V. Joseph, C.F. Wu, Michael Glinsky, Sonata Valaitis, (2022). Towards Z-Next: The Integration of Theory, Experiments, and Computational Simulation in a Bayesian Data Assimilation Framework https://doi.org/10.2172/1891191 Publication ID: 80266
Anh Tran, Jing Sun, Dehao Liu, Yan Wang, Timothy Wildey, (2022). A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution Journal of Computing and Information Science in Engineering https://doi.org/10.1115/1.4054237 Publication ID: 80523
Sze Ngai, Wei Tang, Anh Tran, Shuai Yuan, (2022). Orthogonal Polynomials Defined by Self-Similar Measures with Overlaps Experimental Mathematics https://doi.org/10.1080/10586458.2020.1743214 Publication ID: 73143
Anh Tran, Julien Tranchida, (2021). Multi-fidelity Gaussian process and Bayesian optimization for materials design: Application to ternary random alloys https://doi.org/10.2172/1898471 Publication ID: 76804
Brian Adams, William Bohnhoff, Keith Dalbey, Mohamed Ebeida, John Eddy, Michael Eldred, Russell Hooper, Patricia Hough, Kenneth Hu, John Jakeman, Mohammad Khalil, Kathryn Maupin, Jason Monschke, Elliott Ridgway, Ahmad Rushdi, Daniel Seidl, John Stephens, Laura Swiler, Anh Tran, Justin Winokur, (2021). Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis (V.6.16 User’s Manual) https://doi.org/10.2172/1868142 Publication ID: 80729
Anh Tran, Michael Eldred, Scott McCann, Yan Wang, (2021). srMO-BO-3GP: A sequential regularized multi-objective Bayesian optimization for constrained design applications using an uncertain Pareto classifier Journal of Mechanical Design https://doi.org/10.1115/1.4052445 Publication ID: 75386
Anh Tran, (2021). Gaussian process and Bayesian optimization – Bridging the gap between theory and practice in materials science https://doi.org/10.2172/1888412 Publication ID: 75748
Timothy Wildey, Troy Butler, John Jakeman, Anh Tran, (2021). Solving Stochastic Inverse Problems for Property-Structure Relationships in Computational Materials Science https://doi.org/10.2172/1890916 Publication ID: 79535
Kathryn Maupin, Anh Tran, Michael Glinsky, (2021). Multi-Output Surrogate Construction for Fusion Simulations https://doi.org/10.2172/1888976 Publication ID: 79567
Anh Tran, (2021). Scalable3-BO: Big Data meets HPC – A scalable parallel high-dimensional Bayesian optimization framework on supercomputers https://doi.org/10.2172/1875390 Publication ID: 79059
Anh Tran, Julien Tranchida, Timothy Wildey, Aidan Thompson, (2021). Multi-fidelity ML/UQ and Bayesian Optimization for Materials Design: Application to Ternary Random Alloys https://doi.org/10.2172/1853874 Publication ID: 77392
Anh Tran, Timothy Wildey, (2021). Solving inverse problems for process-structure linkages using asynchronous parallel Bayesian optimization https://doi.org/10.2172/1854075 Publication ID: 77439
Anh Tran, Hoang Tran, (2021). Microstructure reconstruction via non-local patch-based image inpainting https://doi.org/10.2172/1854430 Publication ID: 77473
Anh Tran, Timothy Wildey, (2021). Solving stochastic inverse problems for property-structure linkage using data-consistent inversion and ML https://doi.org/10.2172/1848050 Publication ID: 77388
Anh Tran, Tim Wildey, (2021). Solving Stochastic Inverse Problems for Structure-Property Linkages Using Data-Consistent Inversion Minerals, Metals and Materials Series https://www.osti.gov/servlets/purl/1825594 Publication ID: 71189
Anh Tran, Tim Wildey, (2021). Solving Inverse Problems for Process-Structure Linkages Using Asynchronous Parallel Bayesian Optimization Minerals, Metals and Materials Series https://www.osti.gov/servlets/purl/1825595 Publication ID: 71190
Anh Tran, Tim Wildey, (2021). Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning JOM https://doi.org/10.1007/s11837-020-04432-w Publication ID: 71200
Anh Tran, (2021). Scalable3-BO: Big data meets HPC – A scalable asynchronous parallel high-dimensional Bayesian optimization framework on supercomputers Proceedings of the ASME Design Engineering Technical Conference https://doi.org/10.1115/DETC2021-70828 Publication ID: 78626
Anh Tran, Hoang Tran, (2020). 2D microstructure reconstruction for SEM via non-local patch-based image inpainting https://www.osti.gov/servlets/purl/1825596 Publication ID: 71191
Anh Tran, Timothy Wildey, Julien Tranchida, Aidan Thompson, (2020). Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys Journal of Chemical Physics https://doi.org/10.1063/5.0015672 Publication ID: 73589
Anh Tran, John Mitchell, Laura Swiler, Tim Wildey, (2020). An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics Acta Materialia https://doi.org/10.1016/j.actamat.2020.04.054 Publication ID: 73364
Anh Tran, Timothy Wildey, Theron Rodgers, (2020). On supervised and unsupervised deep learning applications for materials informatics https://www.osti.gov/servlets/purl/1812467 Publication ID: 74399
Anh Tran, Michael Eldred, Yan Wang, Scott McCann, (2020). srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications https://doi.org/10.1115/DETC2020-22184 Publication ID: 74268
Anh Tran, Robert Visintainer, John Furlan, Krishnan Pagalthivarthi, Mohamed Garman, Aaron Cutright, Yan Wang, (2020). WearGP: A UQ/ML wear prediction framework for slurry impellers and casings https://www.osti.gov/servlets/purl/1808256 Publication ID: 74042
Anh Tran, Tim Wildey, Scott McCann, (2020). sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications Journal of Computing and Information Science in Engineering https://doi.org/10.1115/1.4046697 Publication ID: 73008
Anh Tran, John Furlan, Robert Visintainer, Mohamed Garman, Krishnan Pagalthivarthi, Aaron Cutright, Yan Wang, (2020). WearGP: A UQ/ML wear prediction framework for slurry pump impellers and casings https://doi.org/10.1115/FEDSM2020-20059 Publication ID: 73173
Anh Tran, Jing Sun, Yan Wang, Dehao Liu, Timothy Wildey, (2020). Multiscale stochastic reduced-order model for uncertainty propagation using Fokker-Planck equation with microstructure evolution applications arXiv preprint https://www.osti.gov/servlets/purl/1834331 Publication ID: 73191
Anh Tran, Michael Eldred, Scott McCann, Yan Wang, (2020). srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications Proceedings of the ASME Design Engineering Technical Conference https://www.osti.gov/servlets/purl/1808255 Publication ID: 74041
Anh Tran, Timothy Wildey, (2019). Materials informatics: data-driven materials design and uncertainty quantification perspectives https://www.osti.gov/servlets/purl/1643479 Publication ID: 66699
Timothy Wildey, Lukas Bruder, Tan Bui-Thanh, Troy Butler, John Jakeman, Brad Marvin, Anh Tran, Scott Walsh, (2019). Moving Beyond Forward Simulation to Enable Data-informed Physics-based Predictions https://www.osti.gov/biblio/1646273 Publication ID: 66318
Anh Tran, Yan Wang, John Furlan, Krishnan Pagalthivarthi, Aaron Cutright, Mohamed Garman, Robert Visintainer, (2019). WearGP: A machine learning wear prediction framework for slurry pump impellers and casings under different operating conditions https://www.osti.gov/servlets/purl/1643296 Publication ID: 66351
Anh Tran, Hoang Tran, (2019). Data-driven high-fidelity 2D microstructure reconstruction via non-local patch-based image inpainting Acta Materialia https://doi.org/10.1016/j.actamat.2019.08.007 Publication ID: 70470
Anh Tran, Timothy Wildey, Scott McCann, (2019). A sequential bi-fidelity constrained Bayesian optimization for design applications https://www.osti.gov/servlets/purl/1641565 Publication ID: 70358
Timothy Wildey, Anh Tran, (2019). Using High Performance Computing to Enable Data-informed Multiscale Modeling with Applications to Additive Materials https://www.osti.gov/servlets/purl/1640837 Publication ID: 69247
Anh Tran, Yan Wang, Timothy Wildey, (2019). A step towards a versatile Bayesian optimization: constrained asynchronous batch-parallel multi-fidelity and mixed-integer extensions https://www.osti.gov/servlets/purl/1640079 Publication ID: 68628
Anh Tran, John Furlan, Krishnan Pagalthivarthi, Robert Visintainer, Tim Wildey, Yan Wang, (2019). WearGP: A computationally efficient machine learning framework for local erosive wear predictions via nodal Gaussian processes Wear https://doi.org/10.1016/j.wear.2018.12.081 Publication ID: 64352
Anh Tran, (2019). Toward a versatile Bayesian optimization https://www.osti.gov/biblio/1644772 Publication ID: 67771
Anh Tran, Tim Wildey, Scott McCann, (2019). SBF-BO-2CoGP: A sequential bi-fidelity constrained Bayesian optimization for design applications Proceedings of the ASME Design Engineering Technical Conference https://doi.org/10.1115/DETC2019-97986 Publication ID: 70480
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