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The Monte Carlo driven and machine learning enhanced process simulator
16 references·0 citing·26 second hop
The Monte Carlo driven and machine learning enhanced process simulator (2019)The Monte Carlo driven and machine learni…SALib: An open-source Python library for Sensitivity Analysis (2017)SALib: An open-source Python …Chaospy: An open source tool for designing methods of uncertainty quantification (2015)Chaospy: An open source tool …Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion (2014)Global Sensitivity Analysis f…Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Variance based sensitivity an…Good modeling practice for PAT applications: Propagation of input uncertainty and sensitivity analysis (2009)Good modeling practice for PA…An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis (2009)An adaptive algorithm to buil…Global Sensitivity Analysis. The Primer (2007)Global Sensitivity Analysis. …Global sensitivity analysis using polynomial chaos expansions (2007)Global sensitivity analysis u…Uncertainty Reporting for Experimental Thermodynamic Properties (2005)Uncertainty Reporting for Exp…Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Latin hypercube sampling and …The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)The Wiener--Askey Polynomial …Making best use of model evaluations to compute sensitivity indices (2002)Making best use of model eval…A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)A Comparison of Three Methods…Analysis of variance designs for model output (1999)Analysis of variance designs …An Introduction to Copulas (1999)An Introduction to CopulasA distribution-free approach to inducing rank correlation among input variables (1982)A distribution-free approach …Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)Correlation and Dependence in Risk Management: Properties and Pitfalls (2002)Stochastic Finite Elements: A Spectral Approach (1991)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)Statistics for Experimenters (1979)Factorial Sampling Plans for Preliminary Computational Experiments (1991)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)Handbook on Constructing Composite Indicators (2005)Design and Analysis of Computer Experiments (1989)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)How to avoid a perfunctory sensitivity analysis (2010)Empirical Model Building and Response Surfaces (1988)A new uncertainty importance measure (2006)Survey of sampling-based methods for uncertainty and sensitivity analysis (2006)A Review on Global Sensitivity Analysis Methods (2015)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)A review of techniques for parameter sensitivity analysis of environmental models (1994)Importance measures in global sensitivity analysis of nonlinear models (1996)UQLab: A Framework for Uncertainty Quantification in Matlab (2014)Quantitative Risk Management: Concepts, Techniques, and Tools (2005)MODELLING ASYMMETRIC EXCHANGE RATE DEPENDENCE* (2006)Introductory Econometrics for Finance (2008)The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making (2017)The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses (2022)
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References

  • 2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code8,233 cited
  • 1999An Introduction to Copulas7,743 cited
  • 2007Global Sensitivity Analysis. The Primer6,425 cited
  • 2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations4,821 cited
  • 2009Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index3,567 cited
  • 2003Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems2,707 cited
  • 2007Global sensitivity analysis using polynomial chaos expansions2,547 cited
  • 2002Making best use of model evaluations to compute sensitivity indices2,225 cited
  • 1982A distribution-free approach to inducing rank correlation among input variables1,625 cited
  • 2017SALib: An open-source Python library for Sensitivity Analysis1,544 cited
  • 2009An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis834 cited
  • 1999Analysis of variance designs for model output648 cited
  • 2015Chaospy: An open source tool for designing methods of uncertainty quantification336 cited
  • 2009Good modeling practice for PAT applications: Propagation of input uncertainty and sensitivity analysis198 cited

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  • 2014Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion117 cited
  • 2005Uncertainty Reporting for Experimental Thermodynamic Properties53 cited
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