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 | 1235 | 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 | 532 | 2017 |
Local Gaussian process approximation for large computer experiments RB Gramacy, DW Apley Journal of Computational and Graphical Statistics 24 (2), 561-578, 2015 | 439 | 2015 |
Quantification of model uncertainty: Calibration, model discrepancy, and identifiability PD Arendt, DW Apley, W Chen | 409 | 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 | 390 | 2018 |
Bayesian optimization for materials design with mixed quantitative and qualitative variables Y Zhang, DW Apley, W Chen Scientific reports 10 (1), 4924, 2020 | 277 | 2020 |
Stochastic microstructure characterization and reconstruction via supervised learning R Bostanabad, AT Bui, W Xie, DW Apley, W Chen Acta Materialia 103, 89-102, 2016 | 228 | 2016 |
Understanding the effects of model uncertainty in robust design with computer experiments DW Apley, J Liu, W Chen | 227 | 2006 |
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 | 206 | 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 | 200 | 2009 |
Diagnosis of multiple fixture faults in panel assembly DW Apley, J Shi | 199 | 1998 |
The GLRT for statistical process control of autocorrelated processes DW Apley, J Shi IIE transactions 31 (12), 1123-1134, 1999 | 187 | 1999 |
Improving identifiability in model calibration using multiple responses PD Arendt, DW Apley, W Chen, D Lamb, D Gorsich | 178 | 2012 |
Adaptive CUSUM procedures with EWMA-based shift estimators W Jiang, L Shu, DW Apley Iie Transactions 40 (10), 992-1003, 2008 | 177 | 2008 |
The Autoregressive T2 Chart for Monitoring Univariate Autocorrelated Processes DW Apley, F Tsung Journal of Quality Technology 34 (1), 80-96, 2002 | 164 | 2002 |
A factor-analysis method for diagnosing variability in mulitvariate manufacturing processes DW Apley, J Shi Technometrics 43 (1), 84-95, 2001 | 147 | 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 | 127 | 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 | 95 | 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 | 92 | 2011 |