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Research Output 1994 2019

2019

A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors

Zhang, Y., Tao, S., Chen, W. & Apley, D., Jan 1 2019, (Accepted/In press) In : Technometrics.

Research output: Contribution to journalArticle

Latent Variables
Process Modeling
Parameterization
Covariance matrix
Gaussian Process
1 Citation (Scopus)

An exploratory analysis approach for understanding variation in stochastic textured surfaces

Bui, A. T. & Apley, D., Sep 1 2019, In : Computational Statistics and Data Analysis. 137, p. 33-50 18 p.

Research output: Contribution to journalArticle

Exploratory Analysis
Dissimilarity
Quality Control
Quality control
Dissimilarity Measure

Identifying and visualizing part-to-part variation with spatially dense optical dimensional metrology data

Shi, Z., Apley, D. & Runger, G. C., Jan 1 2019, In : Journal of Quality Technology. 51, 1, p. 3-20 18 p.

Research output: Contribution to journalArticle

Quality control
Surface measurement
Specifications
Wealth
Dimensionality

Input mapping for model calibration with application to wing aerodynamics

Tao, S., Apley, D., Chen, W., Garbo, A., Pate, D. J. & German, B. J., Jan 1 2019, In : AIAA journal. 57, 7, p. 2734-2745 12 p.

Research output: Contribution to journalArticle

Aerodynamics
Calibration
Vortex flow
Geometry

Projection-free kernel principal component analysis for denoising

Bui, A. T., Im, J. K., Apley, D. & Runger, G. C., Sep 10 2019, In : Neurocomputing. 357, p. 163-176 14 p.

Research output: Contribution to journalArticle

Principal Component Analysis
Principal component analysis
Observation

Technometrics 2018 Editor's Report

Apley, D., Jan 2 2019, In : Technometrics. 61, 1, p. 2-6 5 p.

Research output: Contribution to journalEditorial

Uncertainty Propagation
Frequency Response Function
Gaussian Model
Gaussian Process
Process Model
2018
5 Citations (Scopus)

A Monitoring and Diagnostic Approach for Stochastic Textured Surfaces

Bui, A. T. & Apley, D., Jan 2 2018, In : Technometrics. 60, 1, p. 1-13 13 p.

Research output: Contribution to journalArticle

Diagnostics
Monitoring
Supervised Learning
Supervised learning
Defects
31 Citations (Scopus)

Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques

Bostanabad, R., Zhang, Y., Li, X., Kearney, T., Brinson, L. C., Apley, D., Liu, W. K. & Chen, W., Jun 1 2018, In : Progress in Materials Science. 95, p. 1-41 41 p.

Research output: Contribution to journalReview article

Microstructure
Materials science
Processing
Unsupervised learning
Spectral density

Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis

Howard, P., Apley, D. W. & Runger, G., Jan 1 2018, In : IEEE Transactions on Neural Networks and Learning Systems. 29, 1, p. 156-166 11 p., 7707373.

Research output: Contribution to journalArticle

Principal component analysis
Neural networks
Backpropagation
1 Citation (Scopus)

Enhanced Collaborative Optimization Using Alternating Direction Method of Multipliers

Tao, S., Shintani, K., Yang, G., Meingast, H., Apley, D. & Chen, W., Oct 1 2018, In : Structural and Multidisciplinary Optimization. 58, 4, p. 1571-1588 18 p.

Research output: Contribution to journalArticle

Method of multipliers
Alternating Direction Method
Optimization Methods
Optimization
Multidisciplinary Design Optimization

Identifying nonlinear variation patterns with deep autoencoders

Howard, P., Apley, D. & Runger, G., Dec 2 2018, In : IISE Transactions. 50, 12, p. 1089-1103 15 p.

Research output: Contribution to journalArticle

Quality control
11 Citations (Scopus)

Leveraging the nugget parameter for efficient Gaussian process modeling

Bostanabad, R., Kearney, T., Tao, S., Apley, D. & Chen, W., May 4 2018, In : International Journal for Numerical Methods in Engineering. 114, 5, p. 501-516 16 p.

Research output: Contribution to journalArticle

Process Modeling
Gaussian Process
Hyperparameters
Gaussian Model
Likelihood Function
2 Citations (Scopus)

Monitoring for changes in the nature of stochastic textured surfaces

Bui, A. T. & Apley, D., Jan 1 2018, In : Journal of Quality Technology. 50, 4, p. 363-378 16 p.

Research output: Contribution to journalArticle

Monitoring
Control surfaces
Supervised learning
Learning algorithms
Textiles
2 Citations (Scopus)

Patchwork kriging for large-scale Gaussian process regression

Park, C. & Apley, D., Jul 1 2018, In : Journal of Machine Learning Research. 19, p. 1-43 43 p.

Research output: Contribution to journalArticle

Kriging
Gaussian Process
Regression
Gaussian Model
Pseudo-observations
2017
69 Citations (Scopus)

A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality

Bessa, M. A., Bostanabad, R., Liu, Z., Hu, A., Apley, D., Brinson, L. C., Chen, W. & Liu, W. K., Jun 15 2017, In : Computer Methods in Applied Mechanics and Engineering. 320, p. 633-667 35 p.

Research output: Contribution to journalArticle

machine learning
Learning systems
experiment design
plastic properties
finite element method

ANOVA models for Brownian motion

Hazen, G. B., Apley, D. & Parikh, N., Aug 3 2017, In : Communications in Statistics - Theory and Methods. 46, 15, p. 7642-7660 19 p.

Research output: Contribution to journalArticle

Brownian motion
Tables
Tumor Growth
Fixed Effects
Covariance Structure
1 Citation (Scopus)

Batch Sample Design from Databases for Logistic Regression

Ouyang, L., Apley, D. & Mehrotra, S., Feb 1 2017, In : Quality and Reliability Engineering International. 33, 1, p. 87-101 15 p.

Research output: Contribution to journalArticle

Logistics
Design of experiments
Sampling
Entropy
Experiments
1 Citation (Scopus)

Fault Tree Analysis: Assessing the Adequacy of Reporting Efforts to Reduce Postoperative Bloodstream Infection

McElroy, L. M., Khorzad, R., Rowe, T. A., Abecassis, Z. A., Apley, D. W., Barnard, C. & Holl, J. L., Jan 1 2017, In : American Journal of Medical Quality. 32, 1, p. 80-86 7 p.

Research output: Contribution to journalArticle

Centers for Medicare and Medicaid Services (U.S.)
Quality Improvement
Infection
Joints
Publications
3 Citations (Scopus)

Lifted Brownian Kriging Models

Plumlee, M. & Apley, D. W., Apr 3 2017, In : Technometrics. 59, 2, p. 165-177 13 p.

Research output: Contribution to journalArticle

Kriging
Computer simulation
Computer Simulation
Model
Covariance Function

Multi-response approach to improving identifiability in model calibration

Jiang, Z., Arendt, P. D., Apley, D. & Chen, W., Jun 16 2017, Handbook of Uncertainty Quantification. Springer International Publishing, p. 69-127 59 p.

Research output: Chapter in Book/Report/Conference proceedingChapter

Model Calibration
Identifiability
Calibration
Discrepancy
Multiple Responses
1 Citation (Scopus)

Technometrics 2017 Editor's Report

Apley, D., Oct 2 2017, In : Technometrics. 59, 4, p. 413-415 3 p.

Research output: Contribution to journalEditorial

2016
4 Citations (Scopus)
Medical Records
Electronic Health Records
Logistic Models
Databases
Sudden Cardiac Death
14 Citations (Scopus)
Calibration
Experiments
Computer simulation
Covariance matrix
Design of experiments
5 Citations (Scopus)
Covariance Function
Process Modeling
Gaussian Process
Basis Functions
Predictors
15 Citations (Scopus)

Characterization and reconstruction of 3D stochastic microstructures via supervised learning

Bostanabad, R., Chen, W. & Apley, D., Dec 1 2016, In : Journal of Microscopy. 264, 3, p. 282-297 16 p.

Research output: Contribution to journalArticle

Learning
Spatial Analysis
Datasets
Direction compound

Designed sampling from large databases for controlled trials

Ouyang, L., Apley, D. & Mehrotra, S., Dec 1 2016, In : IIE Transactions (Institute of Industrial Engineers). 48, 12, p. 1087-1097 11 p.

Research output: Contribution to journalArticle

Sampling
Design of experiments
Marketing
Sales
Optimal design
5 Citations (Scopus)

Discovering the Nature of Variation in Nonlinear Profile Data

Shi, Z., Apley, D. & Runger, G. C., Jul 2 2016, In : Technometrics. 58, 3, p. 371-382 12 p.

Research output: Contribution to journalArticle

Monitoring
Supervised learning
Animation
Quality control
Websites
3 Citations (Scopus)

Estimating the density of a conditional expectation

Steckley, S. G., Henderson, S. G., Ruppert, D., Yang, R., Apley, D. & Staum, J., Jan 1 2016, In : Electronic Journal of Statistics. 10, 1, p. 736-760 25 p.

Research output: Contribution to journalArticle

Conditional Expectation
Estimator
Kernel Density Estimation
Convergence Results
Sample Size
Learning systems
Calibration
Random processes
Sensitivity analysis
9 Citations (Scopus)

Nonhierarchical multi-model fusion using spatial random processes

Chen, S., Jiang, Z., Yang, S., Apley, D. & Chen, W., May 18 2016, In : International Journal for Numerical Methods in Engineering. 106, 7, p. 503-526 24 p.

Research output: Contribution to journalArticle

Spatial Process
Multi-model
Random process
Random processes
Fidelity
8 Citations (Scopus)

Reduction of Epistemic Model Uncertainty in Simulation-Based Multidisciplinary Design

Jiang, Z., Chen, S., Apley, D. & Chen, W., Aug 1 2016, In : Journal of Mechanical Design, Transactions Of the ASME. 138, 8, 081403.

Research output: Contribution to journalArticle

Uncertainty analysis
Sensitivity analysis
Resource allocation
Decision making
Electronics packaging
59 Citations (Scopus)

Stochastic microstructure characterization and reconstruction via supervised learning

Bostanabad, R., Bui, A. T., Xie, W., Apley, D. & Chen, W., Jan 15 2016, In : Acta Materialia. 103, p. 89-102 14 p.

Research output: Contribution to journalArticle

Supervised learning
Microstructure
Materials science
Pixels
2015
15 Citations (Scopus)
Random processes
Electronics packaging
Uncertainty
Uncertainty analysis
1 Citation (Scopus)

A Structural Equation Modeling based approach for identifying key descriptors in microstructural materials design

Zhang, Y., Apley, D. & Chen, W., Jan 1 2015, 41st Design Automation Conference. American Society of Mechanical Engineers (ASME), (Proceedings of the ASME Design Engineering Technical Conference; vol. 2B-2015).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Material Design
Structural Equation Modeling
Descriptors
Microstructure
Materials properties
57 Citations (Scopus)

Local Gaussian Process Approximation for Large Computer Experiments

Gramacy, R. B. & Apley, D., Jan 1 2015, In : Journal of Computational and Graphical Statistics. 24, 2, p. 561-578 18 p.

Research output: Contribution to journalArticle

Computer Experiments
Gaussian Process
Approximation
Predictors
Feature Modeling

New Metrics for Validation of Data-Driven Random Process Models in Uncertainty Quantification

Xu, H., Jiang, Z., Apley, D. & Chen, W., Dec 10 2015, In : ASME Journal of Verification, Validation and Uncertainty Quantification. 1, 2, p. 011002-1—011002-14 14 p.

Research output: Contribution to journalArticle

Random processes
Stochastic models
Chaos theory
Materials properties
Uncertainty
1 Citation (Scopus)

Resource allocation for reduction of epistemic uncertainty in simulation-based multidisciplinary design

Jiang, Z., Chen, S., Apley, D. & Chen, W., Jan 1 2015, 41st Design Automation Conference. American Society of Mechanical Engineers (ASME), (Proceedings of the ASME Design Engineering Technical Conference; vol. 2B-2015).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Epistemic Uncertainty
Resource Allocation
Resource allocation
Uncertainty
Simulation
4 Citations (Scopus)
Model Calibration
Identifiability
Calibration
Multiple Responses
Uncertainty Quantification
2014
11 Citations (Scopus)

A system uncertainty propagation approach with model uncertainty quantification in multidisciplinary design

Jiang, Z., Li, W., Apley, D. & Chen, W., Jan 1 2014, 40th Design Automation Conference. American Society of Mechanical Engineers (ASME), Vol. 2B.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Uncertainty Propagation
Uncertainty Quantification
Model Uncertainty
Epistemic Uncertainty
Uncertainty
11 Citations (Scopus)

Feature selection for noisy variation patterns using kernel principal component analysis

Sahu, A., Apley, D. & Runger, G. C., Jan 1 2014, In : Knowledge-Based Systems. 72, p. 37-47 11 p.

Research output: Contribution to journalArticle

Principal component analysis
Feature extraction
Kernel
Feature selection
8 Citations (Scopus)

Fractional Brownian fields for response surface metamodeling

Zhang, N. & Apley, D., Jan 1 2014, In : Journal of Quality Technology. 46, 4, p. 285-301 17 p.

Research output: Contribution to journalArticle

Computer simulation
Covariance matrix
Data structures
Statistics
Random field
8 Citations (Scopus)

Preimages for variation patterns from kernel PCA and bagging

Shinde, A., Sahu, A., Apley, D. & Runger, G., May 1 2014, In : IIE Transactions (Institute of Industrial Engineers). 46, 5, p. 429-456 28 p.

Research output: Contribution to journalArticle

Principal component analysis
Inspection
Availability
Industry
2013
19 Citations (Scopus)
Sampling
Interpolation
Uncertainty
Simulators
Computer simulation
6 Citations (Scopus)

Preposterior analysis to select experimental responses for improving identifiability in model uncertainty quantification

Jiang, Z., Chen, W. & Apley, D., Jan 1 2013, 39th Design Automation Conference. American Society of Mechanical Engineers, Vol. 3 B. V03BT03A051

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Uncertainty Quantification
Identifiability
Model Uncertainty
Calibration
Multiple Responses
2012
15 Citations (Scopus)

A time-dependent proportional hazards survival model for credit risk analysis

Im, J. K., Apley, D., Qi, C. & Shan, X., Mar 1 2012, In : Journal of the Operational Research Society. 63, 3, p. 306-321 16 p.

Research output: Contribution to journalArticle

Risk analysis
Hazards
Maximum likelihood
Logistics
Industry
57 Citations (Scopus)

Improving identifiability in model calibration using multiple responses

Arendt, P. D., Apley, D., Chen, W., Lamb, D. & Gorsich, D., Oct 8 2012, In : Journal of Mechanical Design, Transactions Of the ASME. 134, 10, 100909.

Research output: Contribution to journalArticle

Calibration
Physics
Uncertainty
Experiments

Objective - Oriented sequential sampling for simulation based robust design considering multiple sources of uncertainty

Arendt, P. D., Chen, W. & Apley, D., Dec 1 2012, ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2012. PARTS A AND B ed. Vol. 3. p. 717-726 10 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sequential Sampling
Robust Design
Sampling
Uncertainty
Interpolate
10 Citations (Scopus)

Posterior distribution charts: A Bayesian approach for graphically exploring a process mean

Apley, D., Aug 1 2012, In : Technometrics. 54, 3, p. 279-293 15 p.

Research output: Contribution to journalArticle

Process Mean
Random errors
Posterior distribution
Chart
Bayesian Approach
132 Citations (Scopus)
Calibration
Uncertainty
Physics