Open Access
Issue |
EPJ Nuclear Sci. Technol.
Volume 5, 2019
|
|
---|---|---|
Article Number | 4 | |
Number of page(s) | 32 | |
DOI | https://doi.org/10.1051/epjn/2018050 | |
Published online | 28 February 2019 |
- C.G. Bucher, H.J. Pradlwarter, G.I. Schuëller, Computational Stochastic Structural Analysis (COSSAN) (Springer, Berlin, Heidelberg, 1991), pp. 301–315 [Google Scholar]
- B.M. Adams, W. Bohnhoff, K. Dalbey, J. Eddy, M. Eldred, D. Gay, K. Haskell, P.D. Hough, L.P. Swiler, Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: version 5.0 users manual, Technical Report SAND2010-2183, Sandia National Laboratories [Google Scholar]
- M. Baudin, R. Lebrun, B. Iooss, A.-L. Popelin, Openturns: An industrial software for uncertainty quantification in simulation, in Handbook of Uncertainty Quantification (Springer, Cham, 2017), pp. 2001–2038 [CrossRef] [Google Scholar]
- S. Marelli, B. Sudret, UQLab: A framework for uncertainty quantification in Matlab, in Proceedings, SIAM Conference on Uncertainty Quantification, Savannah, GA, USA (ETH-Zürich, 2014), pp. 2554–2563 [Google Scholar]
- R. Brun, F. Rademakers, Nucl. Instrum. Methods A389, 81 (1997) [CrossRef] [Google Scholar]
- E. de Rocquigny, N. Devictor, S. Tarantola, Uncertainty in Industrial Practice: A Guide to Quantitative Uncertainty Management (John Wiley & Sons, NJ, 2008) [CrossRef] [Google Scholar]
- K. Martin, B. Hoffman, IEEE Software 24, 46 (2007) [CrossRef] [Google Scholar]
- T. Kluyver, B. Ragan-Kelley, F. Pérez, B. Granger, M. Bussonnier, J. Frederic, K. Kelley, J. Hamrick, J. Grout, S. Corlay, P. Ivanov, D. Avila, S. Abdalla, C. Willing, Jupyter notebooks − a publishing format for reproducible computational workflows, in Positioning and Power in Academic Publishing: Players, Agents and Agendas, edited by F. Loizides, B. Schmidt (IOS Press, Amsterdam, 2016), pp. 87–90 [Google Scholar]
- M. Feathers, B. Lepilleur, Cppunit cookbook (2002), http://cppunit.sourceforge.net/doc/1.8.0/cppunit_cookbook.html [Google Scholar]
- J.C. Meza, R.A. Oliva, P.D. Hough, P.J. Williams, ACM Trans. Math. Softw. 33, 12 (2007) [CrossRef] [Google Scholar]
- M. Frigo, S.G. Johnson, Proc. IEEE 93, 216 (2005) (special issue on “Program Generation, Optimization, and Platform Adaptation”) [Google Scholar]
- S.G. Johnson, The nlopt nonlinear-optimization package, 2008, http://ab-initio.mit.edu/nlopt [Google Scholar]
- E. Gabriel, G.E. Fagg, G. Bosilca, T. Angskun, J.J. Dongarra, J.M. Squyres, V. Sahay, P. Kambadur, B. Barrett, A. Lumsdaine, R.H. Castain, D.J. Daniel, R.L. Graham, T.S. Woodall, Open MPI: Goals, concept, and design of a next generation MPI implementation, in Proceedings, 11th European PVM/MPI Users' Group Meeting, Budapest, Hungary , 2004, pp. 97–104 [Google Scholar]
- C. Nvidia, Nvidia Corporation 120, 8 (2011) [Google Scholar]
- D. Van Heesch, Doxygen: Source code documentation generator tool, 2008, http://www.doxygen.org [Google Scholar]
- J.-B. Blanchard, Methodological reference guide for uranie v3.11.0, Technical Report, CEA, DEN/DANS/DM2S/STMF/LGLS/RT/17-006/A, updated version provided in the source of the Uranie platform for every new release [Google Scholar]
- M.D. McKay, R.J. Beckman, W.J. Conover, Technometrics 42, 55 (2000) [CrossRef] [Google Scholar]
- D. Morris, J. Mitchell, J. Stat. Plan. Inference 43, 381 (1995) [CrossRef] [Google Scholar]
- L. Pronzato, W. Muller, Stat. Comput. 22, 681 (2012) [CrossRef] [Google Scholar]
- G. Damblin, M. Couplet, B. Iooss, J. Simul. 7, 276 (2013) [CrossRef] [Google Scholar]
- R.L. Iman, W.J. Conover, Commun. Stat. Simul. Comput. 11, 311 (1982) [CrossRef] [Google Scholar]
- J.H. Halton, Commun. ACM 7, 701 (1964) [CrossRef] [Google Scholar]
- I. Sobol', USSR Comput. Math. Math. Phys. 7, 86 (1967) [CrossRef] [Google Scholar]
- K. Petras, Numer. Algorithms 26, 93 (2001) [CrossRef] [Google Scholar]
- A. De Crécy, P. Bazin, Determination of the uncertainties of the constitutive relationship of the CATHARE 2 code (M&C, 2001) [Google Scholar]
- K.-T. Fang, R. Li, A. Sudjianto, Design and Modeling for Computer Experiments, Computer Science & Data Analysis Series (Chapman & Hall/CRC, Boca Raton, 2005) [Google Scholar]
- N. Wiener, Am. J. Math. 60, 897 (1938) [CrossRef] [MathSciNet] [Google Scholar]
- R.H. Cameron, W.T. Martin, Ann. Math. 48, 385 (1947) [CrossRef] [Google Scholar]
- R.G. Ghanem, P.D. Spanos, Stochastic Finite Elements: A Spectral Approach (Springer-Verlag, New York, 1991) [Google Scholar]
- M. Baudin, J.-M. Martinez, Polynômes de chaos sous Scilab via la librairie NISP, in 42èmes Journées de Statistique, Marseille, France, 2010, https://hal.inria.fr/inria-00494680 [Google Scholar]
- W. McCulloch, W. Pitts, Bull. Math. Biophys. 5, 115 (1943) [CrossRef] [MathSciNet] [Google Scholar]
- F. Rosenblatt, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Report (Cornell Aeronautical Laboratory) (Spartan Books, Washington DC, 1962) [Google Scholar]
- C.E. Rasmussen, C.K. Williams, Gaussian Process for Machine Learning (MIT Press, MA, 2006) [Google Scholar]
- G. Matheron, La théorie des variables régionalisées, et ses applications, Fasicule 5 in Les Cahiers du Centre de Morphologie Mathématique de Fontainebleau [Google Scholar]
- F. Bachoc, Estimation paramétrique de la fonction de covariance dans le modèle de krigeage par processus gaussiens: application à la quantification des incertitues en simulation numérique, Ph.D. thesis, Mathématiques appliquées, Paris 7, thèse de doctorat dirigée par Garnier, Josselin, 2013 [Google Scholar]
- R.M. Neal et al., Mcmc using hamiltonian dynamics, in Handbook of Markov Chain Monte Carlo 2 (11) (Chapman and Hall/CRC) [Google Scholar]
- T. Robinson, F. Fallside, Compu. Speech Lang. 5, 259 (1991) [CrossRef] [Google Scholar]
- G.E. Hinton, Prog. Brain Res. 165, 535 (2007) [CrossRef] [Google Scholar]
- M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng, TensorFlow: Large-scale machine learning on heterogeneous systems, software, available from tensorflow.org (2015), https://www.tensorflow.org/ [Google Scholar]
- B. Iooss, P. Lemaître, A review on global sensitivity analysis methods, in Uncertainty Management in Simulation-Optimization of Complex Systems: Algorithms and Applications, edited by C. Meloni, G. Dellino (Springer, NY, 2015), pp. 101–122 [CrossRef] [Google Scholar]
- R. Ghanem, D. Higdon, H. Owhadi (Eds.), Springer Handbook on Uncertainty Quantification (Springer, Cham, 2017) [CrossRef] [Google Scholar]
- A. Saltelli, K. Chan, E. Scott, Sensitivity Analysis (Wiley, New York, 2008) [Google Scholar]
- S. Da Veiga, J. Stat. Comput. Simul. 85, 1283 (2015) [CrossRef] [Google Scholar]
- B. Bettonvil, J.P. Kleijnen, Eur. J. Oper. Res. 96, 180 (1997) [CrossRef] [Google Scholar]
- A. Saltelli, S. Tarantola, F. Campolongo, M. Ratto, T. Andres, J. Cariboni, D. Gatelli, M. Saisana, Global Sensitivity Analysis: The Primer (Wiley, New York, 2008) [Google Scholar]
- A. Saltelli, S. Tarantola, F. Campolongo, M. Ratto, Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models (Wiley, New York, 2004) [Google Scholar]
- T. Homma, A. Saltelli, Reliab. Eng. Syst. Saf. 52, 1 (1996) [CrossRef] [Google Scholar]
- G. McRae, J. Tilden, J. Seinfeld, Comput. Chem. Eng. 6, 15 (1982) [CrossRef] [Google Scholar]
- A. Saltelli, R. Bolado, Comput. Stat. Data Anal. 26, 445 (1998) [CrossRef] [Google Scholar]
- A. Saltelli, Comput. Phys. Commun. 145, 280 (2002) [CrossRef] [Google Scholar]
- H. Monod, C. Naud, D. Makowski, Uncertainty and sensitivity analysis for crop models, in Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, edited by D. Wallach, D. Makowski, J.W. Jones (Elsevier, Amsterdam, 2006) [Google Scholar]
- J.-M. Martinez, Analyse de sensibilité globale par décomposition de la variance, Technical Report, GdR Ondes et Mascot Num, institut Henri Poincaré, 2011 [Google Scholar]
- R. Iman, M. Shortencarier, J. Johnson, FORTRAN 77 program and users guide for the calculation of partial correlation and standardized regression coefficients, Sandia National Laboratories, 1985 [Google Scholar]
- S. Tarantola, D. Gatelli, T. Mara, Reliab. Eng. Syst. Saf. 91, 717 (2006) [CrossRef] [Google Scholar]
- J.-Y. Tissot, C. Prieur, Reliab. Eng. Syst. Saf. 107, 205 (2012) [CrossRef] [Google Scholar]
- M.D. McKay, J.D. Morrison, S.C. Upton et al., Comput. Phys. Commun. 117, 44 (1999) [CrossRef] [Google Scholar]
- T. Alex Mara, O. Rakoto Joseph, J. Stat. Comput. Simul. 78, 167 (2008) [CrossRef] [Google Scholar]
- A.B. Owen, SIAM/ASA J. Uncertain. Quantif. 2, 245 (2014) [Google Scholar]
- P. Lemaître, E. Sergienko, A. Arnaud, N. Bousquet, F. Gamboa, B. Iooss, J. Stat. Comput. Simul. 85, 1200 (2015) [CrossRef] [Google Scholar]
- D.R. Jones, M. Schonlau, W.J. Welch, J. Glob. Optim. 13, 455 (1998) [CrossRef] [MathSciNet] [Google Scholar]
- X. Zhang, Y. Tian, Y. Jin, IEEE Trans. Evol. Comput. 19, 761 (2015) [CrossRef] [Google Scholar]
- E. Zitzler, S. Künzli, Indicator-based selection in multiobjective search, in International Conference on Parallel Problem Solving from Nature (Springer, Heidelberg, 2004), pp. 832–842 [Google Scholar]
- Q. Zhang, H. Li, IEEE Trans. Evol. Comput. 11, 712 (2007) [CrossRef] [Google Scholar]
- U. Drepper, What Every Programmer Should Know About Memory (2007) [Google Scholar]
- M.J. Bayarri, J.O. Berger, R. Paulo, J. Sacks, J.A. Cafeo, J. Cavendish, C.-H. Lin, J. Tu, Technometrics 49, 138 (2007) [CrossRef] [Google Scholar]
- M.C. Kennedy, A. O’Hagan, J. R. Stat. Soc. 63, 425 (2001) [Google Scholar]
- F. Bachoc, G. Bois, J. Garnier, J.-M. Martinez, Nucl. Sci. Eng. 176, 81 (2014) [CrossRef] [Google Scholar]
- G. Casella, E.I. George, Am. Stat. 46, 167 (1992) [Google Scholar]
- S. Chib, E. Greenberg, Am. Stat. 49, 327 (1995) [Google Scholar]
- Y.-G. Zhao, T. Ono, Struct. Saf. 21, 95 (1999) [CrossRef] [Google Scholar]
- A.M. Hasofer, N.C. Lind, J. Eng. Mech. Div. 100, 111 (1974) [Google Scholar]
- S.-K. Au, J.L. Beck, Probab. Eng. Mech. 16, 263 (2001) [CrossRef] [MathSciNet] [Google Scholar]
- X. Huang, J. Chen, H. Zhu, Struct. Saf. 59, 86 (2016) [CrossRef] [Google Scholar]
- A.N. Kolmogorov, Giornale dell'Istituto Italiano degli Attuari 4, 83 (1933) [Google Scholar]
- T.W. Anderson, D.A. Darling, Ann. Math. Stat. 23, 193 (1952) [CrossRef] [Google Scholar]
- T.W. Anderson, Ann. Math. Stat. 33, 1148 (1962) [CrossRef] [Google Scholar]
- S. Nanty, C. Helbert, A. Marrel, N. Pérot, C. Prieur, Comput. Stat. 32, 559 (2017) [CrossRef] [Google Scholar]
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