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Gradient-Enhanced Universal Kriging for Uncertainty Propagation
16 references·5 citing·41 second hop
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this work references cited by second hop

References

  • 1989Design and Analysis of Computer Experiments6,958 cited
  • 1991Stochastic Finite Elements: A Spectral Approach5,753 cited
  • 1999Interpolation of Spatial Data2,362 cited
  • 2000Interpolation of Spatial Data: Some Theory for Kriging1,916 cited
  • 2007A Framework for Validation of Computer Models656 cited
  • 2004Combining Field Data and Computer Simulations for Calibration and Prediction622 cited
  • 2004Challenge problems: uncertainty in system response given uncertain parameters570 cited
  • 2004Uncertainty propagation using Wiener–Haar expansions438 cited
  • 1998Stochastic Response Surface Methods (SRSMs) for Uncertainty Propagation: Application to Environmental and Biological Systems387 cited
  • 1993Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction299 cited
  • 2002Efficient Uncertainty Analysis Methods for Multidisciplinary Robust Design198 cited
  • 2002Efficient uncertainty analysis methods for multidisciplinary robust design184 cited
  • 2005Sensitivity and Uncertainty Analysis, Volume II141 cited
  • 2000Efficient Sensitivity/Uncertainty Analysis Using the Combined Stochastic Response Surface Method and Automated Differentiation: Application to Environmental and Biological Systems105 cited

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2004
Probabilistic Formulations for Transferring Inferences from Mathematical Models to Physical Systems
105 cited
  • 2010Polynomial Regression Approaches Using Derivative Information for Uncertainty Quantification88 cited
  • Cited by

    • 2012Gradient-based methods for uncertainty quantification in hypersonic flows63 cited
    • 2018Efficient uncertainty quantification for a hypersonic trailing-edge flap, using gradient-enhanced kriging25 cited
    • 2017Multi-Fidelity Uncertainty Analysis in CFD Using Hierarchical Kriging25 cited
    • 2012Mixed Aleatory/Epistemic Uncertainty Quantification for Hypersonic Flows via Gradient-Based Optimization and Surrogate Models21 cited
    • 2023Sensitivity-enhanced generalized polynomial chaos for efficient uncertainty quantification15 cited