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PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling
16 references·1 citing·19 second hop
PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling (2023)PyApprox: A software package for sensitiv…The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)The Future of Sensitivity Ana…SALib: An open-source Python library for Sensitivity Analysis (2017)SALib: An open-source Python …Handbook of Uncertainty Quantification (2016)Handbook of Uncertainty Quant…UQLab: A Framework for Uncertainty Quantification in Matlab (2014)UQLab: A Framework for Uncert…Simulation-based optimal Bayesian experimental design for nonlinear systems (2012)Simulation-based optimal Baye…Adaptive sparse polynomial chaos expansion based on least angle regression (2010)Adaptive sparse polynomial ch…Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Variance based sensitivity an…A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data (2008)A Sparse Grid Stochastic Coll…Global sensitivity analysis using polynomial chaos expansions (2007)Global sensitivity analysis u…Stochastic spectral methods for efficient Bayesian solution of inverse problems (2006)Stochastic spectral methods f…Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach (2004)Probabilistic Sensitivity Ana…The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)The Wiener--Askey Polynomial …Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Global sensitivity indices fo…Stochastic Finite Elements: A Spectral Approach (1991)Stochastic Finite Elements: A…Factorial Sampling Plans for Preliminary Computational Experiments (1991)Factorial Sampling Plans for …Design and Analysis of Computer Experiments (1989)Design and Analysis of Comput…Uncertainty Quantification for In Silico Chemistry (2026)Uncertainty Quantification fo…A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Efficient computation of global sensitivity indices using sparse polynomial chaos expansions (2010)POLYNOMIAL-CHAOS-BASED KRIGING (2015)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)Global Sensitivity Analysis. The Primer (2007)How to avoid a perfunctory sensitivity analysis (2010)Importance measures in global sensitivity analysis of nonlinear models (1996)A new uncertainty importance measure (2006)Bayesian analysis of computer code outputs: A tutorial (2006)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)AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Taking the Human Out of the Loop: A Review of Bayesian Optimization (2015)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)The Exploratory Modeling Workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making (2017)Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses (2022)
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References

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  • 2007Global sensitivity analysis using polynomial chaos expansions2,547 cited
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  • 2010Adaptive sparse polynomial chaos expansion based on least angle regression1,507 cited
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  • 2016Handbook of Uncertainty Quantification588 cited

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2006
Stochastic spectral methods for efficient Bayesian solution of inverse problems
471 cited
  • 2012Simulation-based optimal Bayesian experimental design for nonlinear systems451 cited
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    • 2026Uncertainty Quantification for In Silico Chemistry13 cited