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
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Tran, A., Maupin, K., Rodgers, T., & Rodgers, T. (2022). Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications. Journal of Computing and Information Science in Engineering, 23(1). https://doi.org/10.1115/1.4055852 Publication ID: 80158
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Tran, A., Wildey, T., Lim, H., & Lim, H. (2022). Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods. Frontiers in Materials, 9. https://doi.org/10.3389/fmats.2022.915254 Publication ID: 80842
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Maupin, K., Tran, A., Lewis, W., Knapp, P., Joseph, V., Wu, C., Glinsky, M., Valaitis, S., & Valaitis, S. (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
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Tran, A., Sun, J., Liu, D., Wang, Y., Wildey, T., & Wildey, T. (2022). A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution. Journal of Computing and Information Science in Engineering, 22(6). https://doi.org/10.1115/1.4054237 Publication ID: 80523
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Ngai, S.-M., Tang, W., Tran, A., Yuan, S., & Yuan, S. (2022). Orthogonal Polynomials Defined by Self-Similar Measures with Overlaps. Experimental Mathematics, 31(3), pp. 1026-1038. https://doi.org/10.1080/10586458.2020.1743214 Publication ID: 73143
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Adams, B., Bohnhoff, W., Dalbey, K., Ebeida, M., Eddy, J., Eldred, M., Hooper, R., Hough, P., Hu, K., Jakeman, J., Khalil, M., Maupin, K., Monschke, J., Ridgway, E., Rushdi, A., Seidl, D., Stephens, J., Swiler, L., Tran, A., Winokur, J., & Winokur, J. (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
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Tran, A., Tranchida, J., & Tranchida, J. (2021). Multi-fidelity Gaussian process and Bayesian optimization for materials design: Application to ternary random alloys [Conference Presenation]. https://doi.org/10.2172/1898471 Publication ID: 76804
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Tran, A., Eldred, M., McCann, S., Wang, Y., & Wang, Y. (2021). srMO-BO-3GP: A sequential regularized multi-objective Bayesian optimization for constrained design applications using an uncertain Pareto classifier. Journal of Mechanical Design, 144(3). https://doi.org/10.1115/1.4052445 Publication ID: 75386
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Tran, A. (2021). Gaussian process and Bayesian optimization – Bridging the gap between theory and practice in materials science [Conference Presenation]. https://doi.org/10.2172/1888412 Publication ID: 75748
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Maupin, K., Tran, A., Glinsky, M., & Glinsky, M. (2021). Multi-Output Surrogate Construction for Fusion Simulations [Conference Presenation]. https://doi.org/10.2172/1888976 Publication ID: 79567
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Wildey, T., Butler, T., Jakeman, J., Tran, A., & Tran, A. (2021). Solving Stochastic Inverse Problems for Property-Structure Relationships in Computational Materials Science [Conference Presenation]. https://doi.org/10.2172/1890916 Publication ID: 79535
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Tran, A. (2021). Scalable3-BO: Big Data meets HPC – A scalable parallel high-dimensional Bayesian optimization framework on supercomputers [Conference Presenation]. https://doi.org/10.2172/1875390 Publication ID: 79059
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Tran, A., Wildey, T., & Wildey, T. (2021). Solving inverse problems for process-structure linkages using asynchronous parallel Bayesian optimization [Conference Presenation]. https://doi.org/10.2172/1854075 Publication ID: 77439
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Tran, A., Tranchida, J., Wildey, T., Thompson, A., & Thompson, A. (2021). Multi-fidelity ML/UQ and Bayesian Optimization for Materials Design: Application to Ternary Random Alloys [Conference Poster]. https://doi.org/10.2172/1853874 Publication ID: 77392
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Tran, A., Tran, H., & Tran, H. (2021). Microstructure reconstruction via non-local patch-based image inpainting [Conference Presenation]. https://doi.org/10.2172/1854430 Publication ID: 77473
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Tran, A., Wildey, T., & Wildey, T. (2021). Solving stochastic inverse problems for property-structure linkage using data-consistent inversion and ML [Conference Poster]. https://doi.org/10.2172/1848050 Publication ID: 77388
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Tran, A., Wildey, T., & Wildey, T. (2021). Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning. JOM, 73(1), pp. 72-89. https://doi.org/10.1007/s11837-020-04432-w Publication ID: 71200
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Tran, A., Wildey, T., & Wildey, T. (2021). Solving Inverse Problems for Process-Structure Linkages Using Asynchronous Parallel Bayesian Optimization [Conference Paper]. Minerals, Metals and Materials Series. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85104385778&origin=inward Publication ID: 71190
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Tran, A., Wildey, T., & Wildey, T. (2021). Solving Stochastic Inverse Problems for Structure-Property Linkages Using Data-Consistent Inversion [Conference Paper]. Minerals, Metals and Materials Series. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85104400551&origin=inward Publication ID: 71189
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Tran, A. (2021). Scalable3-BO: Big data meets HPC – A scalable asynchronous parallel high-dimensional Bayesian optimization framework on supercomputers [Conference Paper]. Proceedings of the ASME Design Engineering Technical Conference. https://doi.org/10.1115/DETC2021-70828 Publication ID: 78626
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Tran, A., Tran, H., & Tran, H. (2020). 2D microstructure reconstruction for SEM via non-local patch-based image inpainting [Conference Paper]. https://www.osti.gov/biblio/1825596 Publication ID: 71191
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Tran, A., Wildey, T., Tranchida, J., Thompson, A., & Thompson, A. (2020). Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys. Journal of Chemical Physics, 153(7). https://doi.org/10.1063/5.0015672 Publication ID: 73589
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Tran, A., Mitchell, J., Swiler, L., Wildey, T., & Wildey, T. (2020). An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics. Acta Materialia, 194, pp. 80-92. https://doi.org/10.1016/j.actamat.2020.04.054 Publication ID: 73364
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Tran, A., Wildey, T., Rodgers, T., & Rodgers, T. (2020). On supervised and unsupervised deep learning applications for materials informatics [Conference Poster]. https://www.osti.gov/biblio/1812467 Publication ID: 74399
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Tran, A., Visintainer, R., Furlan, J., Pagalthivarthi, K., Garman, M., Cutright, A., Wang, Y., & Wang, Y. (2020). WearGP: A UQ/ML wear prediction framework for slurry impellers and casings [Conference Poster]. https://www.osti.gov/biblio/1808256 Publication ID: 74042
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Tran, A., Eldred, M., Wang, Y., McCann, S., & McCann, S. (2020). srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications [Conference Poster]. https://doi.org/10.1115/DETC2020-22184 Publication ID: 74268
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Tran, A., Wildey, T., McCann, S., & McCann, S. (2020). sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications. Journal of Computing and Information Science in Engineering, 20(3). https://doi.org/10.1115/1.4046697 Publication ID: 73008
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Tran, A., Furlan, J., Visintainer, R., Garman, M., Pagalthivarthi, K., Cutright, A., Wang, Y., & Wang, Y. (2020). WearGP: A UQ/ML wear prediction framework for slurry pump impellers and casings [Conference Poster]. https://doi.org/10.1115/FEDSM2020-20059 Publication ID: 73173
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Tran, A., Sun, J., Wang, Y., Liu, D., Wildey, T., & Wildey, T. (2020). Multiscale stochastic reduced-order model for uncertainty propagation using Fokker-Planck equation with microstructure evolution applications. arXiv preprint. https://www.osti.gov/biblio/1834331 Publication ID: 73191
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Tran, A., Eldred, M., McCann, S., Wang, Y., & Wang, Y. (2020). srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications [Conference Poster]. Proceedings of the ASME Design Engineering Technical Conference. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85094669306&origin=inward Publication ID: 74041
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Tran, A., Wildey, T., & Wildey, T. (2019). Materials informatics: data-driven materials design and uncertainty quantification perspectives [Conference Poster]. https://www.osti.gov/biblio/1643479 Publication ID: 66699
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Tran, A., Wang, Y., Furlan, J., Pagalthivarthi, K., Cutright, A., Garman, M., Visintainer, R., & Visintainer, R. (2019). WearGP: A machine learning wear prediction framework for slurry pump impellers and casings under different operating conditions [Conference Poster]. https://www.osti.gov/biblio/1643296 Publication ID: 66351
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Wildey, T., Bruder, L., Bui-Thanh, T., Butler, T., Jakeman, J., Marvin, B., Tran, A., Walsh, S., & Walsh, S. (2019). Moving Beyond Forward Simulation to Enable Data-informed Physics-based Predictions [Presentation]. https://www.osti.gov/biblio/1646273 Publication ID: 66318
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Tran, A., Tran, H., & Tran, H. (2019). Data-driven high-fidelity 2D microstructure reconstruction via non-local patch-based image inpainting. Acta Materialia, 178, pp. 207-218. https://doi.org/10.1016/j.actamat.2019.08.007 Publication ID: 70470
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Tran, A., Wildey, T., McCann, S., & McCann, S. (2019). A sequential bi-fidelity constrained Bayesian optimization for design applications [Conference Poster]. https://www.osti.gov/biblio/1641565 Publication ID: 70358
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Wildey, T., Tran, A., & Tran, A. (2019). Using High Performance Computing to Enable Data-informed Multiscale Modeling with Applications to Additive Materials [Conference Poster]. https://www.osti.gov/biblio/1640837 Publication ID: 69247
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Tran, A., Wang, Y., Wildey, T., & Wildey, T. (2019). A step towards a versatile Bayesian optimization: constrained asynchronous batch-parallel multi-fidelity and mixed-integer extensions [Conference Poster]. https://www.osti.gov/biblio/1640079 Publication ID: 68628
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Tran, A., Furlan, J.M., Pagalthivarthi, K.V., Visintainer, R.J., Wildey, T., Wang, Y., & Wang, Y. (2019). WearGP: A computationally efficient machine learning framework for local erosive wear predictions via nodal Gaussian processes. Wear, 422-423, pp. 9-26. https://doi.org/10.1016/j.wear.2018.12.081 Publication ID: 64352
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Tran, A. (2019). Toward a versatile Bayesian optimization [Presentation]. https://www.osti.gov/biblio/1644772 Publication ID: 67771
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Tran, A., Wildey, T., McCann, S., & McCann, S. (2019). SBF-BO-2CoGP: A sequential bi-fidelity constrained Bayesian optimization for design applications [Conference Poster]. Proceedings of the ASME Design Engineering Technical Conference. https://doi.org/10.1115/DETC2019-97986 Publication ID: 70480