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

Kay2016
  1. Kay, T. Kola, J. Hullman, S. Munson. When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems. CHI 2016. DOI: 10.1145/2858036.2858558

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.

Plischke2012

Plischke, E., An adaptive correlation ratio method using the cumulative sum of the reordered output, Reliability Engineering & System Safety, vol. 107, pp. 149–156, 2012.

Dupuis2018

Dupuis, R., Aerodynamic Data Predictions for Transonic Flows via a Machine-Learning-based Surrogate Model, 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. 2018. DOI: 10.2514/6.2018-1905