| Visualizing the effects of predictor variables in black box supervised learning models DW Apley, J Zhu Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2020 | 1031 | 2020 |
| A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality MA Bessa, R Bostanabad, Z Liu, A Hu, DW Apley, C Brinson, W Chen, ... Computer Methods in Applied Mechanics and Engineering 320, 633-667, 2017 | 484 | 2017 |
| Local Gaussian process approximation for large computer experiments RB Gramacy, DW Apley Journal of Computational and Graphical Statistics 24 (2), 561-578, 2015 | 415 | 2015 |
| Quantification of model uncertainty: Calibration, model discrepancy, and identifiability PD Arendt, DW Apley, W Chen | 393 | 2012 |
| Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques R Bostanabad, Y Zhang, X Li, T Kearney, LC Brinson, DW Apley, WK Liu, ... Progress in Materials Science 95, 1-41, 2018 | 352 | 2018 |
| Bayesian optimization for materials design with mixed quantitative and qualitative variables Y Zhang, DW Apley, W Chen Scientific reports 10 (1), 4924, 2020 | 239 | 2020 |
| Understanding the effects of model uncertainty in robust design with computer experiments DW Apley, J Liu, W Chen | 225 | 2006 |
| Stochastic microstructure characterization and reconstruction via supervised learning R Bostanabad, AT Bui, W Xie, DW Apley, W Chen Acta Materialia 103, 89-102, 2016 | 218 | 2016 |
| A non‐stationary covariance‐based Kriging method for metamodelling in engineering design Y Xiong, W Chen, D Apley, X Ding International Journal for Numerical Methods in Engineering 71 (6), 733-756, 2007 | 204 | 2007 |
| A better understanding of model updating strategies in validating engineering models Y Xiong, W Chen, KL Tsui, DW Apley Computer methods in applied mechanics and engineering 198 (15-16), 1327-1337, 2009 | 198 | 2009 |
| Diagnosis of multiple fixture faults in panel assembly DW Apley, J Shi | 198 | 1998 |
| The GLRT for statistical process control of autocorrelated processes DW Apley, J Shi IIE transactions 31 (12), 1123-1134, 1999 | 184 | 1999 |
| Improving identifiability in model calibration using multiple responses PD Arendt, DW Apley, W Chen, D Lamb, D Gorsich | 176 | 2012 |
| Adaptive CUSUM procedures with EWMA-based shift estimators W Jiang, L Shu, DW Apley Iie Transactions 40 (10), 992-1003, 2008 | 163 | 2008 |
| The Autoregressive T2 Chart for Monitoring Univariate Autocorrelated Processes DW Apley, F Tsung Journal of Quality Technology 34 (1), 80-96, 2002 | 157 | 2002 |
| A factor-analysis method for diagnosing variability in mulitvariate manufacturing processes DW Apley, J Shi Technometrics 43 (1), 84-95, 2001 | 148 | 2001 |
| A latent variable approach to Gaussian process modeling with qualitative and quantitative factors Y Zhang, S Tao, W Chen, DW Apley Technometrics 62 (3), 291-302, 2020 | 112 | 2020 |
| Design of exponentially weighted moving average control charts for autocorrelated processes with model uncertainty DW Apley, H Cheol Lee Technometrics 45 (3), 187-198, 2003 | 111 | 2003 |
| Patchwork kriging for large-scale gaussian process regression C Park, D Apley Journal of Machine Learning Research 19 (7), 1-43, 2018 | 90 | 2018 |
| Efficient nested simulation for estimating the variance of a conditional expectation Y Sun, DW Apley, J Staum Operations research 59 (4), 998-1007, 2011 | 88 | 2011 |
