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UQit: A Python package for uncertainty quantification (UQ) in computational fluid dynamics (CFD)
9 references·0 citing·25 second hop
UQit: A Python package for uncertainty quantification (UQ) in computational fluid dynamics (CFD) (2021)UQit: A Python package for uncertainty qu…An uncertainty-quantification framework for assessing accuracy, sensitivity, and robustness in computational fluid dynamics (2022)An uncertainty-quantification…Handbook of Uncertainty Quantification (2017)Handbook of Uncertainty Quant…Handbook of Uncertainty Quantification (2016)Handbook of Uncertainty Quant…Uncertainty Quantification: Theory, Implementation, and Applications (2013)Uncertainty Quantification: T…Numerical Methods for Stochastic Computations: A Spectral Method Approach (2010)Numerical Methods for Stochas…The Design and Analysis of Computer Experiments (2003)The Design and Analysis of Co…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…Design and Analysis of Computer Experiments (1989)Design and Analysis of Comput…Stochastic Finite Elements: A Spectral Approach (1991)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)Global sensitivity analysis using polynomial chaos expansions (2007)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)Multilevel Monte Carlo methods and applications to elliptic PDEs with random coefficients (2011)Stochastic finite element methods for partial differential equations with random input data (2014)Chaospy: An open source tool for designing methods of uncertainty quantification (2015)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Taking the Human Out of the Loop: A Review of Bayesian Optimization (2015)A new uncertainty importance measure (2006)SALib: An open-source Python library for Sensitivity Analysis (2017)An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis (2018)Design and Analysis of Simulation Experiments (2018)Random forests for global sensitivity analysis: A selective review (2020)Uncertainty quantification and sensitivity analysis of the SST turbulence model applied to VIV (2024)A global surrogate model technique based on principal component analysis and Kriging for uncertainty propagation of dynamic systems (2020)Quantitative Storytelling in the Making of a Composite Indicator (2020)Modeling, analysis, and optimization under uncertainties: a review (2021)An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (2011)A Review on Global Sensitivity Analysis Methods (2015)Sensitivity analysis: A review of recent advances (2015)A unified framework for stochastic optimization (2018)Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey (2020)A state-of-the-art review on uncertainty analysis of rotor systems (2022)
this work references cited by second hop

References

  • 1989Design and Analysis of Computer Experiments6,958 cited
  • 2001Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates6,377 cited
  • 2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations4,821 cited
  • 2003The Design and Analysis of Computer Experiments2,204 cited
  • 2013Uncertainty Quantification: Theory, Implementation, and Applications1,147 cited
  • 2010Numerical Methods for Stochastic Computations: A Spectral Method Approach1,107 cited
  • 2016Handbook of Uncertainty Quantification588 cited
  • 2017Handbook of Uncertainty Quantification329 cited
  • 2022An uncertainty-quantification framework for assessing accuracy, sensitivity, and robustness in computational fluid dynamics33 cited

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