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Uncertainty quantification and global sensitivity analysis of complex chemical process using a generalized polynomial chaos approach
8 references·7 citing·29 second hop
Uncertainty quantification and global sensitivity analysis of complex chemical process using a generalized polynomial chaos approach (2016)Uncertainty quantification and global sen…Monte Carlo and Quasi-Monte Carlo Methods (2013)Monte Carlo and Quasi-Monte C…Sensitivity study of dynamic systems using polynomial chaos (2012)Sensitivity study of dynamic …Polynomial chaos expansion for sensitivity analysis (2008)Polynomial chaos expansion fo…Global Sensitivity Analysis. The Primer (2007)Global Sensitivity Analysis. …Distributional uncertainty analysis using power series and polynomial chaos expansions (2007)Distributional uncertainty an…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…Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review (2023)Propagating input uncertainti…Data-driven robust optimization for minimum nitrogen oxide emission under process uncertainty (2021)Data-driven robust optimizati…The Monte Carlo driven and machine learning enhanced process simulator (2019)The Monte Carlo driven and ma…Knowledge-inspired operational reliability for optimal LNG production at the offshore site (2019)Knowledge-inspired operationa…Global Sensitivity Analysis and Uncertainty Quantification of Crude Distillation Unit Using Surrogate Model Based on Gaussian Process Regression (2018)Global Sensitivity Analysis a…Measuring the reliability of a natural gas refrigeration plant: Uncertainty propagation and quantification with polynomial chaos expansion based sensitivity analysis (2017)Measuring the reliability of …Uncertainty quantification and global sensitivity analysis of complex chemical processes with a large number of input parameters using compressive polynomial chaos (2016)Uncertainty quantification an…An efficient method for parametric uncertainty analysis of numerical geophysical models (1997)Stochastic Response Surface Methods (SRSMs) for Uncertainty Propagation: Application to Environmental and Biological Systems (1998)Linear quadratic regulation of systems with stochastic parameter uncertainties (2009)UNCERTAINTY QUANTIFICATION IN STOCHASTIC SYSTEMS USING POLYNOMIAL CHAOS EXPANSION (2010)Wiener's Polynomial Chaos for the Analysis and Control of Nonlinear Dynamical Systems with Probabilistic Uncertainties [Historical Perspectives] (2013)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)Global sensitivity analysis using polynomial chaos expansions (2007)Dynamic system uncertainty propagation using polynomial chaos (2014)Space-partition method for the variance-based sensitivity analysis: Optimal partition scheme and comparative study (2014)Accurate Reduced Dimensional Polynomial Chaos for Efficient Uncertainty Quantification of Microwave/RF Networks (2017)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion (2012)Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions (2017)Sensitivity and uncertainty analysis for flexoelectric nanostructures (2018)Large Sample Properties of Simulations Using Latin Hypercube Sampling (1987)Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics (2008)Monte Carlo Methods in Financial Engineering (2003)Brownian Motion and Stochastic Calculus (2007)Multilevel Monte Carlo simulation for Lévy processes based on the Wiener–Hopf factorisation (2013)不确定性量化的高精度数值方法和理论 (2015)Numerical solution of stochastic differential equations (2016)Entropy-based adaptive design for contour finding and estimating reliability (2022)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)SALib: An open-source Python library for Sensitivity Analysis (2017)AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)A new uncertainty importance measure (2006)
this work references cited by second hop

References

  • 2007Global Sensitivity Analysis. The Primer6,425 cited
  • 2001Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates6,377 cited
  • 1991Stochastic Finite Elements: A Spectral Approach5,753 cited
  • 2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations4,821 cited
  • 2008Polynomial chaos expansion for sensitivity analysis747 cited
  • 2007Distributional uncertainty analysis using power series and polynomial chaos expansions195 cited
  • 2013Monte Carlo and Quasi-Monte Carlo Methods177 cited
  • 2012Sensitivity study of dynamic systems using polynomial chaos60 cited

Cited by

  • 2021Data-driven robust optimization for minimum nitrogen oxide emission under process uncertainty29 cited
  • 2023Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review25 cited
  • 2018Global Sensitivity Analysis and Uncertainty Quantification of Crude Distillation Unit Using Surrogate Model Based on Gaussian Process Regression22 cited
  • 2019Knowledge-inspired operational reliability for optimal LNG production at the offshore site17 cited
  • 2016Uncertainty quantification and global sensitivity analysis of complex chemical processes with a large number of input parameters using compressive polynomial chaos15 cited
  • 2019

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The Monte Carlo driven and machine learning enhanced process simulator
15 cited
  • 2017Measuring the reliability of a natural gas refrigeration plant: Uncertainty propagation and quantification with polynomial chaos expansion based sensitivity analysis13 cited