A software framework for probabilistic sensitivity analysis for computationally expensive models

Advances in Engineering Software - Tập 100 - Trang 19-31 - 2016
N. Vu-Bac1, T. Lahmer1, X. Zhuang2,3, T. Nguyen-Thoi4,5, T. Rabczuk6,5
1Institute of Structural Mechanics, Bauhaus-Universität Weimar, Marienstr. 15, D-99423 Weimar, Germany
2State key laboratory for Disaster Reduction in Civil Engineering, College of Civil Engineering, Tongji University, Shanghai, China
3Institute of Continuum Mechanics, Leibniz University Hannover, Germany
4Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
5Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
6Division of Computational Mechanics, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Tài liệu tham khảo

Mara, 2012, Variance-based sensitivity indices for models with dependent inputs, Rel. Eng. Syst. Safety, 107, 115, 10.1016/j.ress.2011.08.008 Saltelli, 2010, Variance based sensitivity analysis of model output. design and estimator for the total sensitivity index, Comput. Phys. Commun., 181, 259, 10.1016/j.cpc.2009.09.018 Vu-Bac, 2015, Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters, Comp. Part B: Eng., 68, 446, 10.1016/j.compositesb.2014.09.008 Xu, 2007, Extending a global sensitivity analysis technique to models with correlated parameters, Comput. Stat. Data Anal., 51, 5579, 10.1016/j.csda.2007.04.003 Xu, 2008, Uncertainty and sensitivity analysis for models with correlated inputs, Rel. Eng. Syst. Safety, 1563, 10.1016/j.ress.2007.06.003 Kucherenko, 2012, Estimation of global sensitivity indices for models with dependent variables, Comput. Phys. Commun., 183, 937, 10.1016/j.cpc.2011.12.020 Most, 2012, Variance-based sensitivity analysis in the presence of correlated input variables Sobol’, 1993, Sensitivity analysis for non-linear mathematical models, Math. Model. Comput. Exp., 1, 407 Schumann E.. http://comisef.wikidot.com/tutorial:correlateduniformvariates. Vu-Bac, 2015, A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites, Comput. Mat. Sci., 96, 520, 10.1016/j.commatsci.2014.04.066 Ruppert, 2003 Ruppert D.. http://people.orie.cornell.edu/davidr/matlab/. Lophaven, 2002, DACE A MATLAB Kriging toolbox McKay, 1979, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 21, 239 Iman, 1980, Small sample sensitivity analysis techniques for computer models with an application to risk assessment, Commun. Stat., A9, 1749, 10.1080/03610928008827996 Wyss, 1998, A user’s guide to LHS: Sandia’s latin hypercube sampling software. Helton, 2003, Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems, Rel. Eng. Syst. Safety, 81, 23, 10.1016/S0951-8320(03)00058-9 Iman, 1982, A distribution-free approach to inducing rank correlation among input variables, Commun. Stat. - Simul. Comput., 11, 311, 10.1080/03610918208812265 Stein, 1987, Large sample properties of simulations using latin hypercube sampling, Technometrics, 29, 143, 10.1080/00401706.1987.10488205 Forrester, 2008 Vu-Bac, 2014, Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations, Mech. Mat., 68, 70, 10.1016/j.mechmat.2013.07.021 Vu-Bac, 2014, Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs), Compos. Part B: Eng., 59, 80, 10.1016/j.compositesb.2013.11.014 Hutchinson, 1985, Smoothing noisy data with spline functions, Numer. Math., 47, 99, 10.1007/BF01389878 Carroll, 2000, Spatially-adaptive penalties for spline fitting, Aust. N. Z. J. Stat., 42, 205, 10.1111/1467-842X.00119 Dixon, 1978, The global optimization problem: an introduction, Towards Global Optim., 2, 1 Surjanovic S., Bingham D.. Virtual library of simulation experiments: test functions and datasets. Retrieved September 7, 2014, from http://www.sfu.ca/~ssurjano. Bucher, 2009, Computational analysis of randomness in structural mechanics, 3 Ishigami, 1990, An importance quantification technique in uncertainty analysis for computer models, 398 Tüfekci, 2014, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods, Int. J. Electr. Power Energ. Syst., 60, 126, 10.1016/j.ijepes.2014.02.027 Ghasemi, 2015, Optimum fiber content and distribution in fiber-reinforced solids using a reliability and NURBS based sequential optimization approach, Struct. Multidiscip. O., 51, 99, 10.1007/s00158-014-1114-y Ghasemi, 2014, Uncertainties propagation in metamodel-based probabilistic optimization of CNT/polymer composite structure using stochastic multi-scale modeling, Comp. Mater. Sci., 85, 295, 10.1016/j.commatsci.2014.01.020 Wu, 2015, Torsional vibrations of a cylindrical foundation embedded in a saturated poroelastic half-space, Front. Struct. Civ. Eng., 9, 194, 10.1007/s11709-015-0292-z Quayum, 2015, Computational model generation and RVE design of self-healing concrete, Front. Struct. Civ. Eng., 9, 383, 10.1007/s11709-015-0320-z