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Robust Optimization of Dynamical Systems with Correlated Random Variables using the Point Estimate Method ⁎ ⁎Financial support of Promotionsprogramm “μ-Props” by MWK Niedersachsen is gratefully acknowledged.
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Robust Optimization of Dynamical Systems with Correlated Random Variables using the Point Estimate Method ⁎ ⁎Financial support of Promotionsprogramm “μ-Props” by MWK Niedersachsen is gratefully acknowledged. (2018)Robust Optimization of Dynamical Systems …A General Framework for Uncertainty Propagation Based on Point Estimate Methods (2014)A General Framework for Uncer…Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion (2012)Data-driven uncertainty quant…Computational methods in optimization considering uncertainties – An overview (2008)Computational methods in opti…A generalization of the Nataf transformation to distributions with elliptical copula (2008)A generalization of the Nataf…Local and Global Sensitivity Analysis for a Reactor Design with Parameter Uncertainty (2004)Local and Global Sensitivity …Hybrid Process Models in Electrochemical Syntheses under Deep Uncertainty (2021)Hybrid Process Models in Elec…An Application of Numerical Integration Techniclues to Statistical Toleraucing (1967)Propagation of uncertainty: Expressions of second and third order uncertainty with third and fourth moments (2007)Spectral Methods for Uncertainty Quantification (2010)Stochastic Finite Elements: A Spectral Approach (1991)Structural Reliability Methods (1996)Estimation of small failure probabilities in high dimensions by subset simulation (2001)Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles (2011)A Chebyshev interval method for nonlinear dynamic systems under uncertainty (2012)Probability-interval hybrid uncertainty analysis for structures with both aleatory and epistemic uncertainties: a review (2017)Exact and Invariant Second-Moment Code Format (1974)Structural Reliability under Incomplete Probability Information (1986)An Introduction to Copulas (1999)Do Rosenblatt and Nataf isoprobabilistic transformations really differ? (2009)OpenTURNS: An Industrial Software for Uncertainty Quantification in Simulation (2015)A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas (2018)Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory (1973)Factorial Sampling Plans for Preliminary Computational Experiments (1991)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)A general first-order global sensitivity analysis method (2007)A derivative based sensitivity measure of failure probability in the presence of epistemic and aleatory uncertainties (2012)Techno-Economic Assessment (TEA) and Life Cycle Costing Analysis (LCCA): discussing methodological steps and integrability (2021)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)An introduction to sensitivity assessment of simulation models (2015)Review of Polynomial Chaos-Based Methods for Uncertainty Quantification in Modern Integrated Circuits (2018)Reliability analysis with stratified importance sampling based on adaptive Kriging (2020)Sensitivity Analysis for Chemical Models (2005)Aleatory or epistemic? Does it matter? (2008)The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020)
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References

  • 2008Computational methods in optimization considering uncertainties – An overview573 cited
  • 2012Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion451 cited
  • 2008A generalization of the Nataf transformation to distributions with elliptical copula163 cited
  • 2004Local and Global Sensitivity Analysis for a Reactor Design with Parameter Uncertainty87 cited
  • 2014A General Framework for Uncertainty Propagation Based on Point Estimate Methods13 cited

Cited by

  • 2021Hybrid Process Models in Electrochemical Syntheses under Deep Uncertainty11 cited

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