Uncertainty Quantification

Surrogate Modeling

• V. Roshan Joseph (2006) “Limit Kriging“. Technometrics 48, 458-466.
• V. Roshan Joseph, Hung, Y., and Sudjianto, A. (2008) “Blind Kriging: A New Method for Developing Metamodels“. ASME Journal of Mechanical Design 130, 031102-1-8. R code and data, MATLAB package.
• V. Roshan Joseph and Kang, L. (2011) “Regression-Based Inverse Distance Weighting with Applications to Computer Experiments“. Technometrics, 53, 254-265.
• Ba, S. and V. Roshan Joseph (2012) “Composite Gaussian Process Models for Emulating Expensive Functions”. Annals of Applied Statistics, 6, 1838-1860. R package.
• Hung, Y., V. Roshan Joseph, and Melkote, S. N. (2015) “Analysis of Computer Experiments with Functional Response“. Technometrics, 57, 35-44.
• Kang, L. and V. Roshan Joseph (2016) “Kernel Approximation: From Regression to Interpolation“. Journal of Uncertainty Quantification, 4, 112-129.
• Plumlee, M. and V. Roshan Joseph. (2018) “Orthogonal Gaussian Process Models”. Statistica Sinica, 28, 601-619.
• Lin, L.-H. and V. Roshan Joseph. (2020). “Transformation and Additivity in Gaussian Processes “. Technometrics, 62, 525-535. R package: TAG.
• Vakayil, A. and V. Roshan Joseph. (2023+). “A Global-Local Approximation Framework for Large-Scale Gaussian Process Modeling“. Technometrics, to appear. R package: twingp. arXiv.
• V. Roshan Joseph. (2023+). “Rational Kriging”. arXiv.

Model Calibration

• V. Roshan Joseph and Melkote, S. N. (2009) “Statistical Adjustments to Engineering Models“. Journal of Quality Technology, 41, 362-375. Slides of JQT invited talk at FTC 2009.
• Chang, C.-J. and V. Roshan Joseph. (2014) “Model Calibration through Minimal Adjustments”. Technometrics, 56, 474-482.
• V. Roshan Joseph and Yan, H. (2015) “Engineering-driven Statistical Adjustment and Calibration”. Technometrics, 57, 257-267.
• Cao, F., Ba, S., Brenneman, W., and V. Roshan Joseph (2018) “Model calibration with censored data“. Technometrics, 60, 255-262.
• Huang, C., Ren, Y., McGuinness, E., Lively, R. P., Losego, M. D., and V. Roshan Joseph (2021). “Bayesian Optimization of Functional Output in Inverse Problems.” Optimization and Engineering, to appear. R codes.
• Krishna, A., V. Roshan Joseph, Ba, S., Brenneman, W. A., and Myers, W. R. (2022). “Robust Experimental Designs for Model Calibration”. Journal of Quality Technology, 54, 441-452.

 

Uncertainty Propagation

• Gul, E., V. Roshan Joseph, Yan, H., and Melkote, S. N. (2018) “Uncertainty Quantification in Machining Simulations Using In Situ Emulator,” Journal of Quality Technology, 50, 253-261.
• Mak, S. and V. Roshan Joseph. (2018) “Support Points”. Annals of Statistics, 46, 2562-2592. R package: support.

Multi-Fidelity Simulations

• Qian, Z., Seepersad, C. C., V. Roshan Joseph, Allen, J. K., and Wu, C. F. J. (2006) “Building Surrogate Models Based on Detailed and Approximate Simulations”. ASME Journal of Mechanical Design 128, 668-677.
• Ba, S., Jain, N., V. Roshan Joseph, and Singh, R. K. (2013) “Integrating Analytical Models with Finite Element Models: An Application in Micromachining”. Journal of Quality Technology, 45, 200-212.
• Yuchi, H. S., V. Roshan Joseph, Wu, C. F. J. (2023). “Design and Analysis of Multi-Fidelity Finite Element Simulations“. ASME Journal of Mechanical Design, 145 (6), 061703.
• Ji, Y., Yuchi, H. S., Soeder, D., Paquet, J.-F.,  Bass, S. A.,  V. Roshan Joseph, Wu, C. F. J., and Mak, S. (2024). “Conglomerate Multi-Fidelity Gaussian Process Modeling, with Application to Heavy-Ion Collisions “. SIAM/ASA Journal on Uncertainty Quantification, to appear.  arXiv.

Sensitivity Analysis

• Xiao, Q., V. Roshan Joseph, and Ray, D. M. (2022). “Maximum One-Factor-At-A-Time  Designs for Screening in Computer Experiments”. Technometrics, to appear. R package: MOFAT