| Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial V Nemani, L Biggio, X Huan, Z Hu, O Fink, A Tran, Y Wang, X Du, ... Mechanical Systems and Signal Processing 205, 110796, 2023 | 107 | 2023 |
| Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: Version 6.16 theory … KR Dalbey, MS Eldred, G Geraci, JD Jakeman, KA Maupin, JA Monschke, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | 87 | 2021 |
| Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys A Tran, J Tranchida, T Wildey, AP Thompson The Journal of Chemical Physics 153 (7), 074705, 2020 | 85 | 2020 |
| pBO-2GP-3B: A batch parallel known/unknown constrained Bayesian optimization with feasibility classification and its applications in computational fluid dynamics A Tran, J Sun, JM Furlan, KV Pagalthivarthi, RJ Visintainer, Y Wang Computer Methods in Applied Mechanics and Engineering 347, 827-852, 2019 | 81 | 2019 |
| Constrained mixed-integer Gaussian mixture Bayesian optimization and its applications in designing fractal and auxetic metamaterials A Tran, M Tran, Y Wang Structural and Multidisciplinary Optimization 59, 2131-2154, 2019 | 68 | 2019 |
| WearGP: A computationally efficient machine learning framework for local erosive wear predictions via nodal Gaussian processes A Tran, JM Furlan, KV Pagalthivarthi, RJ Visintainer, T Wildey, Y Wang Wear 422, 9-26, 2019 | 56 | 2019 |
| sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications A Tran, T Wildey, S McCann ASME Journal of Computing and Information Science in Engineering, 1-15, 2020 | 55 | 2020 |
| Solving stochastic inverse problems for property-structure linkages using data-consistent inversion and machine learning A Tran, T Wildey JOM 73, 72-89, 2020 | 50 | 2020 |
| An active learning high-throughput microstructure calibration framework for solving inverse structure-process problems in materials informatics A Tran, JA Mitchell, LP Swiler, T Wildey Acta Materialia 194, 80-92, 2020 | 49* | 2020 |
| Computational optimization study of transcatheter aortic valve leaflet design using porcine and bovine leaflets S Travaglino, K Murdock, A Tran, C Martin, L Liang, Y Wang, W Sun Journal of Biomechanical Engineering 142 (1), 2020 | 36 | 2020 |
| aphBO-2GP-3B: a budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture A Tran, M Eldred, T Wildey, S McCann, J Sun, RJ Visintainer Structural and Multidisciplinary Optimization 65 (4), 1-45, 2022 | 31* | 2022 |
| Reliable molecular dynamics: uncertainty quantification using interval analysis in molecular dynamics simulation AV Tran, Y Wang Computational Materials Science 127, 141-160, 2017 | 30 | 2017 |
| srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications A Tran, M Eldred, S McCann, Y Wang ASME 2019 International Design Engineering Technical Conferences and …, 2020 | 28 | 2020 |
| An efficient first-principles saddle point searching method based on distributed kriging metamodels A Tran, L He, Y Wang ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B …, 2018 | 26 | 2018 |
| Data-driven high-fidelity 2D microstructure reconstruction via non-local patch-based image inpainting A Tran, H Tran Acta Materialia 178, 207-218, 2019 | 25 | 2019 |
| Quantifying uncertainty in the process-structure relationship for Al-Cu solidification AV Tran, D Liu, HA Tran, Y Wang Modelling and Simulation in Materials Science and Engineering 27, 064005, 2019 | 21 | 2019 |
| sBF-BO-2CoGP: A sequential bi-fidelity constrained Bayesian optimization for design applications A Tran, T Wildey, S McCann International Design Engineering Technical Conferences and Computers and …, 2019 | 18 | 2019 |
| Dakota: a multilevel parallel object-oriented framework for design optimization, parameter estimation, etc.: Version 6.0 User’s Manual BM Adams, LE Bauman, WJ Bohnhoff, KR Dalbey, MS Ebeida, JP Eddy, ... Sandia National Lab. Tech. Rept. SAND2014-4633, Albuquerque, New Mexico, 2015 | 17 | 2015 |
| Microstructure-sensitive uncertainty quantification for crystal plasticity finite element constitutive models using stochastic collocation methods A Tran, T Wildey, H Lim Frontiers in Materials 9, 2022 | 13 | 2022 |
| Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods A Tran, P Robbe, H Lim Materialia 27, 101705, 2023 | 12 | 2023 |
Tim WildeySandia National Labs在 sandia.gov 的电子邮件经过验证