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On the Statistical Calibration of Physical Models
7 references·16 citing·33 second hop
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this work references cited by second hop

References

  • 1991Stochastic Finite Elements: A Spectral Approach5,753 cited
  • 2008Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics769 cited
  • 2002A Stochastic Projection Method for Fluid Flow503 cited
  • 2004Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes451 cited
  • 2003Uncertainty quantification in reacting-flow simulations through non-intrusive spectral projection333 cited
  • 2011Bayesian uncertainty analysis with applications to turbulence modeling267 cited
  • 2011The method of uncertainty quantification and minimization using polynomial chaos expansions230 cited

Cited by

  • 2020Embedded Model Error Representation for Bayesian Model Calibration59 cited
  • 2021An extended polynomial chaos expansion for PDF characterization and variation with aleatory and epistemic uncertainties55 cited
  • 2018Representing Model Inadequacy: A Stochastic Operator Approach42 cited
  • 2020Model discrepancy calibration across experimental settings41 cited
  • 2017The parameter uncertainty inflation fallacy41 cited
  • 2018Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations34 cited
  • 2018Hierarchical Stochastic Model in Bayesian Inference for Engineering Applications: Theoretical Implications and Efficient Approximation

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33 cited
  • 2023Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials32 cited
  • 2022OptEx: An integrated framework for experimental design and combustion kinetic model optimization27 cited
  • 2020Modeling strength and failure variability due to porosity in additively manufactured metals23 cited
  • 2017Polynomial Chaos: Modeling, Estimation, and Approximation21 cited
  • 2023Stochastic modeling and statistical calibration with model error and scarce data16 cited
  • 2019Bayesian modeling of inconsistent plastic response due to material variability15 cited
  • 2021Learning Quantities of Interest from dynamical systems for observation-consistent inversion11 cited
  • 2016Polynomial Chaos: Modeling, Estimation, and Approximation11 cited
  • 2021Model error propagation from experimental to prediction configuration10 cited