Bibliography

[Rasmussen2006]CE. Rasmussen and C. Williams: Gaussian processes for machine learning. MIT Press. 2006. ISBN: 026218253X
[Najm2009]
    1. Najm, Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics, Annual Review of Fluid Mechanics 41 (1) (2009) 35–52. DOI:10.1146/annurev.fluid.010908.165248.
[Gunes2006]
  1. Gunes, S. Sirisup and GE. Karniadakis: “Gappydata:ToKrigornottoKrig?”. Journal of Com putational Physics. 2006. DOI:10.1016/j.jcp.2005.06.023
[Draper1995]
  1. Draper: “Assessmentand Propagation ofModelUncertainty”. Journal of the Royal Statistical Society. 1995.
[AnindyaChatterjee2000]Anindya Chatterjee. “An introduction to the proper orthogonal decomposition”. Current Science 78.7. 2000.
[Cordier2006]
  1. Cordierand M. Bergmann. “Réduction de dynamique par décomposition orthogonale aux valeurs propres (POD)”. Ecole de printemps OCET. 2006.
[Damblin2013]
  1. Damblin, M. Couplet, B. Iooss: Numerical studies of space filling designs : optimization of Latin Hypercube Samples and subprojection properties. Journal of Simulation. 2013
[Sacks1989]
  1. Sacks et al.: Design and Analysis of Computer Experiments. Statistical Science 4.4. 1989. DOI: 10.1214/ss/1177012413
[Scheidt]
  1. Scheidt: Analyse statistique d’expériences simulées : Modélisation adaptative de réponses non régulières par Krigeage et plans d’expériences, Application à la quantification des incertitudes en ingénierie des réservoirs pétroliers. Université Louis Pasteur. 2006
[Roy2017]P.T. Roy et al.: Resampling Strategies to Improve Surrogate Model-based Uncertainty Quantification - Application to LES of LS89. IJNMF. 2017
[Jones1998]
  1. Jones et al.: Efficient Global Optimization of Expensive Black-Box Functions. Journal of Global Optimization 1998. DOI: 10.1023/a:1008306431147
[Krige1989]D.G. Krige, et al. “Early South African geostatistical techniques in today’s perspective”. Geostatistics 1. 1989.
[Matheron1963]
  1. Matheron. “Principles of Geostatistics”. Economic Geology 58. 1963.
[Robinson1991]G.K.Robinson.“That BLUP is a good thing: the estimation of random effects”. Statistical Science 6.1. 1991. DOI: 10.1214/ss/1177011926.
[Bohling2005]
  1. Bohling. “Kriging”. Tech.rep. 2005.
[Forrester2006]Forrester, Alexander I.J, et al. “Optimization using surrogate models and partially converged computational fluid dynamics simulations”. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science. 2006. DOI: 10.1098/rspa.2006.1679
[Forrester2009]Forrester and A.J. Keane.“Recent advances in surrogate-based optimization”. Progress in Aerospace Sciences 2009. DOI: 10.1016/j.paerosci.2008.11.001
[iooss2015]Iooss B. and Saltelli A.: Introduction to Sensitivity Analysis. Handbook of UQ. 2015. DOI: 10.1007/978-3-319-11259-6_31-1
[ferretti2016]Ferretti F. and Saltelli A. et al.: Trends in sensitivity analysis practice in the last decade. Science of the Total Environment. 2016. DOI: 10.1016/j.scitotenv.2016.02.133
[Sobol1993]Sobol’ I.M. Sensitivity analysis for nonlinear mathematical models. Mathematical Modeling and Computational Experiment. 1993.
[iooss2010]Iooss B. et al.: Numerical studies of the metamodel fitting and validation processes. International Journal on Advances in Systems and Measurements. 2010
[marrel2015]Marrel A. et al.: Sensitivity Analysis of Spatial and/or Temporal Phenomena. Handbook of Uncertainty Quantification. 2015. DOI: 10.1007/978-3-319-11259-6_39-1
[baudin2016]Baudin M. et al.: Numerical stability of Sobol’ indices estimation formula. 8th International Conference on Sensitivity Analysis of Model Output. 2016.
[Hyndman2009]Rob J. Hyndman and Han Lin Shang. Rainbow plots, bagplots and boxplots for functional data. Journal of Computational and Graphical Statistics, 19:29-45, 2009
[Hullman2015]Jessica Hullman and Paul Resnick and Eytan Adar. Hypothetical Outcome Plots Outperform Error Bars and Violin Plots for Inferences About Reliability of Variable Ordering. PLoS ONE 10(11): e0142444. 2015. DOI: 10.1371/journal.pone.0142444
[Hackstadt1994]Steven T. Hackstadt and Allen D. Malony and Bernd Mohr. Scalable Performance Visualization for Data-Parallel Programs. IEEE. 1994. DOI: 10.1109/SHPCC.1994.296663
[Wand1995]M.P. Wand and M.C. Jones. Kernel Smoothing. 1995. DOI: 10.1007/978-1-4899-4493-1
[Roy2017b]P.T. Roy et al.: Comparison of Polynomial Chaos and Gaussian Process surrogates for uncertainty quantification and correlation estimation of spatially distributed open-channel steady flows. SERRA. 2017. DOI: 10.1007/s00477-017-1470-4
[Blatman2009phd]Blatman, G., Adaptative sparse Polynomial Chaos expansions for uncertainty propagation and sensitivity analysis, Universit’e Blaise Pascal, Clermont-Ferrand, 2009.
[Lemaitreknio2010]Le Maitre, O. and Knio, O., Spectral Methods for Uncertainty Quantification, Springer, 2010.
[Migliorati2013]Migliorati, G. and Nobile, F. and Von Schwerin, E. and Tempone, R., Approximation of quantities of interest in stochastic PDEs by the random Discret L2 Projection on polynomial spaces, SIAM J. Sci Comput., 35(3), pp. A1440-A1460, 2013.
[Sudret2008]Sudret, B., Global sensitivity analysis using polynomial chaos expansions, Reliab. Eng. Sys. Safety, 93, pp. 964–979, 2008.
[Xiu2010]Xiu, D., Numerical Methods for Stochastic Computations: A Spectral Method Approach, Princeton University Press, 2010.
[Xiu2002]Xiu, D. and Karniadakis, G.E., The Wiener–Askey Polynomial Chaos for Stochastic Differential Equations, SIAM Journal on Scientific Computing, 24 (2), pp. 619-644, 2002.