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Uncertainty Quantification in Computational Science
12 references·0 citing·33 second hop
Uncertainty Quantification in Computational Science (2014)Uncertainty Quantification in Computation…Padé–Legendre approximants for uncertainty analysis with discontinuous response surfaces (2009)Padé–Legendre approximants fo…Numerical analysis of the Burgers’ equation in the presence of uncertainty (2009)Numerical analysis of the Bur…A TVD Uncertainty Quantification Method with Bounded Error Applied to Transonic Airfoil Flutter (2009)A TVD Uncertainty Quantificat…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…On the construction and analysis of stochastic models: Characterization and propagation of the errors associated with limited data (2006)On the construction and analy…High-Order Collocation Methods for Differential Equations with Random Inputs (2005)High-Order Collocation Method…Uncertainty quantification in reacting-flow simulations through non-intrusive spectral projection (2003)Uncertainty quantification in…Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Latin hypercube sampling and …The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)The Wiener--Askey Polynomial …Investigation of Evidence Theory for Engineering Applications (2002)Investigation of Evidence The…Stochastic Finite Elements: A Spectral Approach (1991)Stochastic Finite Elements: A…Advances in the Dempster-Shafer theory of evidence (1994)Different methods are needed to propagate ignorance and variability (1996)Error and uncertainty in modeling and simulation (2002)The Value of Using Imprecise Probabilities in Engineering Design (2005)A response surface approach for structural reliability analysis using evidence theory (2014)Structural reliability analysis using a copula-function-based evidence theory model (2014)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)Sensitivity analysis: A review of recent advances (2015)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)Modeling uncertainty in flow simulations via generalized polynomial chaos (2003)Stochastic model reduction for chaos representations (2007)A better understanding of model updating strategies in validating engineering models (2008)A least-squares approximation of partial differential equations with high-dimensional random inputs (2009)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)A Stochastic Projection Method for Fluid Flow (2002)Point-Collocation Nonintrusive Polynomial Chaos Method for Stochastic Computational Fluid Dynamics (2010)The method of uncertainty quantification and minimization using polynomial chaos expansions (2011)Simulation-based optimal Bayesian experimental design for nonlinear systems (2012)A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data (2007)Non-Intrusive Polynomial Chaos Methods for Uncertainty Quantification in Fluid Dynamics (2010)A stochastic Galerkin method for the Euler equations with Roe variable transformation (2013)Robust Uncertainty Propagation in Systems of Conservation Laws with the Entropy Closure Method (2013)Large Sample Properties of Simulations Using Latin Hypercube Sampling (1987)Empirical Model Building and Response Surfaces (1988)Design and Analysis of Computer Experiments (1989)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Survey of sampling-based methods for uncertainty and sensitivity analysis (2006)A Review on Global Sensitivity Analysis Methods (2015)AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics (2008)DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's manual. (2010)Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization (2018)
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References

  • 1991Stochastic Finite Elements: A Spectral Approach5,753 cited
  • 2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations4,821 cited
  • 2003Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems2,707 cited
  • 2007Global sensitivity analysis using polynomial chaos expansions2,547 cited
  • 2005High-Order Collocation Methods for Differential Equations with Random Inputs1,522 cited
  • 2008A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data942 cited
  • 2003Uncertainty quantification in reacting-flow simulations through non-intrusive spectral projection333 cited
  • 2006On the construction and analysis of stochastic models: Characterization and propagation of the errors associated with limited data191 cited
  • 2002Investigation of Evidence Theory for Engineering Applications106 cited
  • 2009Padé–Legendre approximants for uncertainty analysis with discontinuous response surfaces76 cited
  • 2009Numerical analysis of the Burgers’ equation in the presence of uncertainty62 cited
  • 2009A TVD Uncertainty Quantification Method with Bounded Error Applied to Transonic Airfoil Flutter29 cited

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