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A Matlab toolbox for Global Sensitivity Analysis
3 references·16 citing·35 second hop
A Matlab toolbox for Global Sensitivity Analysis (2015)A Matlab toolbox for Global Sensitivity A…Global Sensitivity Analysis. The Primer (2007)Global Sensitivity Analysis. …Factorial Sampling Plans for Preliminary Computational Experiments (1991)Factorial Sampling Plans for …Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory (1973)Study of the sensitivity of c…sensobol : An R Package to Compute Variance-Based Sensitivity Indices (2022)sensobol : An R Package to Co…Sensitivity analysis using Morris: Just screening or an effective ranking method? (2021)Sensitivity analysis using Mo…The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)The Future of Sensitivity Ana…Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach (2020)Socio-technical scales in soc…Sensitivity analysis methods in the biomedical sciences (2020)Sensitivity analysis methods …Matlab/R workflows to assess critical choices in Global Sensitivity Analysis using the SAFE toolbox (2019)Matlab/R workflows to assess …How successfully is open-source research software adopted? Results and implications of surveying the users of a sensitivity analysis toolbox (2019)How successfully is open-sour…Certain trends in uncertainty and sensitivity analysis: An overview of software tools and techniques (2019)Certain trends in uncertainty…VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis (2018)VARS-TOOL: A toolbox for comp…Distribution-based sensitivity analysis from a generic input-output sample (2018)Distribution-based sensitivit…A Bayesian Monte Carlo-based method for efficient computation of global sensitivity indices (2018)A Bayesian Monte Carlo-based …Adaptive sparse polynomial chaos expansions for global sensitivity analysis based on support vector regression (2017)Adaptive sparse polynomial ch…Most computational hydrology is not reproducible, so is it really science? (2016)Most computational hydrology …Global Sensitivity Analysis of environmental models: Convergence and validation (2016)Global Sensitivity Analysis o…Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling (2015)Uncertainty quantification of…A guide to uncertainty quantification and sensitivity analysis for cardiovascular applications (2015)A guide to uncertainty quanti…Design and Analysis of Computer Experiments (1989)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Editorial: Special Issue on Verification, Validation, and Uncertainty Quantification of Cardiovascular Models: Towards Effective VVUQ for Translating Cardiovascular Modelling to Clinical Utility (2018)Practical identifiability and uncertainty quantification of a pulsatile cardiovascular model (2018)Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience (2018)The Proof and Measurement of Association between Two Things (1904)Making best use of model evaluations to compute sensitivity indices (2002)Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models (2017)What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling (2019)The Logic of Scientific Discovery. (1959)Why Most Published Research Findings Are False (2005)Why Most Published Research Findings Are False (2005)The Predictive Sample Reuse Method with Applications (1975)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)An efficient method for moment-independent global sensitivity analysis by dimensional reduction technique and principle of maximum entropy (2018)Dynamic reliability analysis using the extended support vector regression (X-SVR) (2019)An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis (2022)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)A COMPREHENSIVE COMPARISON OF TOTAL-ORDER ESTIMATORS FOR GLOBAL SENSITIVITY ANALYSIS (2021)Sensitivity analysis: A discipline coming of age (2021)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Correlation Coefficients: Appropriate Use and Interpretation (2018)Comparing methods of randomizing Sobol′ sequences for improving uncertainty of metrics in variance-based global sensitivity estimation (2021)Multi-method global sensitivity analysis of mathematical models (2022)THE DESIGN OF OPTIMUM MULTIFACTORIAL EXPERIMENTS (1946)On the Experimental Attainment of Optimum Conditions (1951)Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)SALib: An open-source Python library for Sensitivity Analysis (2017)Identification and Review of Sensitivity Analysis Methods (2002)Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach (2004)Model-based design of experiments for parameter precision: State of the art (2007)Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations (2022)Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses (2022)
this work references cited by second hop

References

  • 2007Global Sensitivity Analysis. The Primer6,425 cited
  • 1991Factorial Sampling Plans for Preliminary Computational Experiments4,936 cited
  • 1973Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory1,041 cited

Cited by

  • 2020The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support632 cited
  • 2016Global Sensitivity Analysis of environmental models: Convergence and validation394 cited
  • 2016Most computational hydrology is not reproducible, so is it really science?233 cited
  • 2020Sensitivity analysis methods in the biomedical sciences168 cited
  • 2015A guide to uncertainty quantification and sensitivity analysis for cardiovascular applications163 cited
  • 2018Distribution-based sensitivity analysis from a generic input-output sample162 cited
  • 2017Adaptive sparse polynomial chaos expansions for global sensitivity analysis based on support vector regression126 cited
  • 2019Certain trends in uncertainty and sensitivity analysis: An overview of software tools and techniques114 cited
  • 2022sensobol : An R Package to Compute Variance-Based Sensitivity Indices110 cited
  • 2018VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis103 cited
  • 2015

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Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling
98 cited
  • 2020Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach88 cited
  • 2019How successfully is open-source research software adopted? Results and implications of surveying the users of a sensitivity analysis toolbox68 cited
  • 2018A Bayesian Monte Carlo-based method for efficient computation of global sensitivity indices65 cited
  • 2021Sensitivity analysis using Morris: Just screening or an effective ranking method?64 cited
  • 2019Matlab/R workflows to assess critical choices in Global Sensitivity Analysis using the SAFE toolbox53 cited