A single-loop kriging surrogate modeling for time-dependent reliability analysis Z Hu, S Mahadevan Journal of Mechanical Design 138 (6), 061406, 2016 | 303 | 2016 |
Time-dependent reliability analysis with joint upcrossing rates Z Hu, X Du Structural and Multidisciplinary Optimization 48, 893-907, 2013 | 256 | 2013 |
Mixed efficient global optimization for time-dependent reliability analysis Z Hu, X Du Journal of Mechanical Design 137 (5), 051401, 2015 | 234 | 2015 |
Global sensitivity analysis-enhanced surrogate (GSAS) modeling for reliability analysis Z Hu, S Mahadevan Structural and Multidisciplinary Optimization 53, 501-521, 2016 | 195 | 2016 |
Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities Z Hu, S Mahadevan The International Journal of Advanced Manufacturing Technology 93, 2855-2874, 2017 | 168 | 2017 |
First order reliability method for time-variant problems using series expansions Z Hu, X Du Structural and Multidisciplinary Optimization 51, 1-21, 2015 | 155 | 2015 |
A sampling approach to extreme value distribution for time-dependent reliability analysis Z Hu, X Du Journal of Mechanical Design 135 (7), 071003, 2013 | 146 | 2013 |
Reliability analysis for hydrokinetic turbine blades Z Hu, X Du Renewable Energy 48, 251-262, 2012 | 146 | 2012 |
A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies A Thelen, X Zhang, O Fink, Y Lu, S Ghosh, BD Youn, MD Todd, ... Structural and Multidisciplinary Optimization 65 (12), 354, 2022 | 142 | 2022 |
Uncertainty quantification in prediction of material properties during additive manufacturing Z Hu, S Mahadevan Scripta materialia 135, 135-140, 2017 | 113 | 2017 |
Simulation-based time-dependent reliability analysis for composite hydrokinetic turbine blades Z Hu, H Li, X Du, K Chandrashekhara Structural and Multidisciplinary Optimization 47, 765-781, 2013 | 109 | 2013 |
First order reliability method with truncated random variables X Du, Z Hu | 100 | 2012 |
A data-driven approach for process optimization of metallic additive manufacturing under uncertainty Z Wang, P Liu, Y Xiao, X Cui, Z Hu, L Chen Journal of Manufacturing Science and Engineering 141 (8), 081004, 2019 | 91 | 2019 |
Deep transfer convolutional neural network and extreme learning machine for lung nodule diagnosis on CT images X Huang, Q Lei, T Xie, Y Zhang, Z Hu, Q Zhou Knowledge-Based Systems 204, 106230, 2020 | 88 | 2020 |
A sequential calibration and validation framework for model uncertainty quantification and reduction C Jiang, Z Hu, Y Liu, ZP Mourelatos, D Gorsich, P Jayakumar Computer Methods in Applied Mechanics and Engineering 368, 113172, 2020 | 85 | 2020 |
Research on fluid-structure interaction for piston/cylinder tribopair of seawater hydraulic axial piston pump in deep-sea environment S Nie, M Guo, F Yin, H Ji, Z Ma, Z Hu, X Zhou Ocean Engineering 219, 108222, 2021 | 69 | 2021 |
Resilience assessment based on time-dependent system reliability analysis Z Hu, S Mahadevan Journal of Mechanical Design 138 (11), 111404, 2016 | 67 | 2016 |
Reliability-based design optimization under stationary stochastic process loads Z Hu, X Du Engineering Optimization 48 (8), 1296-1312, 2016 | 59 | 2016 |
A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives A Thelen, X Zhang, O Fink, Y Lu, S Ghosh, BD Youn, MD Todd, ... Structural and multidisciplinary optimization 66 (1), 1, 2023 | 57 | 2023 |
Sensor placement for calibration of spatially varying model parameters P Nath, Z Hu, S Mahadevan Journal of Computational Physics 343, 150-169, 2017 | 57 | 2017 |