Open Access
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 68, Number 3, May-June 2013
Dossier: Discovery and Optimization of Catalysts and Solvents for Absorption Using High Throughput Experimentation
Page(s) 545 - 556
Published online 13 June 2013
  • Busby D., Feraille M. (2008) Adaptive Design of Experiments for calibration of complex simulators – An Application to Uncertainty Quantification of a Mature Oil Field, J. Phys.: Conf. Ser. 135, 012026, doi: 10.1088/1742-6596/135/1/012026.
  • Busby D. (2009) Hierarchical adaptive experimental design for Gaussian process emulators, Reliab. Eng. Syst. Safe. 94, 1183-1193. doi: 10.1016/j.ress.2008.07.007. [CrossRef]
  • Cressie N.A.C. (1993) Statistics for Spatial Data, Wiley, New York.
  • Fang K.T., Li R., Sudjianto A. (2006) Design and modeling for computer experiments, Chapman and Hall/CRC.
  • Feraille M., Marrel A. (2012) Prediction under uncertainty on a mature field, Oil Gas Sci. Technol. 67, 2, 193-206. doi: 10.2516/ogst/2011172. [CrossRef] [EDP Sciences]
  • Geyer C.J. (1992) Practical Markov chain Monte Carlo (with discussion), Stat. Sci. 7, 473-511. doi:10.1214/ss/1177011137. [CrossRef]
  • Hansen N. (2006) The CMA Evolution Strategy: A Comparing Review, in Towards a new evolutionary computation, Advances in estimation of distribution algorithms, Lozano J.A., Larranga P., Inza I., Bengoetxea E. (eds), Springer.
  • Jourdan A. (2000) Analyse statistique et échantillonnage d’expériences simulées, Dissertation, Université de Pau et des pays de l’Adour, France.
  • Kennedy M.C., O’Hagan A. (2001) Bayesian calibration of computer models, J. R. Stat. Soc., Ser. B Stat. Methodol. 63 425-464. doi: 10.1111/1467-9868.00294. [CrossRef] [MathSciNet]
  • McKay M.D., Beckman R.J., Conover W.J. (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics 21 239-245. doi: 10.2307/1268522. [CrossRef] [MathSciNet]
  • Marrel A. (2008) Mise en oeuvre et utilisation du métamodèle processus Gaussien pour l’analyse de sensibiliteé de modèles numeériques, Dissertation, Institut National des Sciences Appliqueées de Toulouse, France.
  • Matheron G. (1963) Principles of geostatistics, Econ. Geol. 58 1246-1266. doi: 10.2113/gsecongeo.58.8.1246. [CrossRef]
  • Sacks J., Welch W.J., Mitchell T.J., Wynn H.P. (1989) Design and analysis of computer experiments, Stat. Sci. 4, 409-423. doi:10.1214/ss/1177012413. [CrossRef] [MathSciNet]
  • Scheidt C., Zabalza-Mezghani I., Feraille M., Collombier D. (2007) Toward a Reliable Quantification of Uncertainty on Production Forecasts: Adaptive Experimental Designs, Oil Gas Sci., Technol. 62, 207-224. doi: 10.2516/ogst:2007018. [CrossRef] [EDP Sciences]
  • Schonlau M. (1997) Computer Experiments and Global Optimization University of Waterloo, Canada, Dissertation.
  • Tarantola A. (2005) Inverse Problem Theory and Methods for Model Parameter Estimation, SIAM (Society for Industrial and Applied Mathematics), ISBN 978-0-89871-572-9.
  • Vazquez E., Bect J. (2010) Convergence properties of the expected improvement algorithm with fixed mean and covariance functions, J. Stat. Plan. Infer. 140, 3088-3095. doi: 10.1016/j.jspi.2010.04.018. [CrossRef]
  • Villemonteix J., Vazquez E., Sidorkiewicz M., Walter E. (2009) Global optimization of expensive-to-evaluate functions: an empirical comparison of two sampling criteria, J. Glob. Optim. 44, 509-534. doi: 10.1007/s10898-008-9313-y. [CrossRef]

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