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What do we hear from a drum? A data-consistent approach to quantifying irreducible uncertainty on model inputs by extracting information from correlated model output data
6 references·0 citing·24 second hop
What do we hear from a drum? A data-consistent approach to quantifying irreducible uncertainty on model inputs by extracting information from correlated model output data (2020)What do we hear from a drum? A data-consi…A new method for model validation with multivariate output (2017)A new method for model valida…Model validation and calibration based on component functions of model output (2015)Model validation and calibrat…A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing (2011)A comprehensive framework for…A top-down approach to calibration, validation, uncertainty quantification and predictive accuracy assessment (2008)A top-down approach to calibr…Validation and error estimation of computational models (2006)Validation and error estimati…Hierarchical Bayesian space-time models (1998)Hierarchical Bayesian space-t…Statistics for Spatial Data (1993)Statistics for Spatial Data. (1993)Statistics for Spatial Data, Revised Edition. (1994)A dimension-reduced approach to space-time Kalman filtering (1999)Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds (2001)Hierarchical Models in Environmental Science (2003)Quantification of Uncertainty in Structural Dynamic Models (2001)Uncertainty Quantification in Verification and Validation of Computational Solid Mechanics Models - Modeling (2009)Learning Quantities of Interest from dynamical systems for observation-consistent inversion (2021)A collaborative model calibration framework under uncertainty considering parameter distribution (2022)Measures of agreement between computation and experiment: Validation metrics (2006)Verification and Validation in Scientific Computing (2010)Verification and Validation in Computational Science and Engineering (1998)Computational methods for model reliability assessment (2007)Simulation and the Monte Carlo Method (2007)Quantitative model validation techniques: New insights (2012)Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems (2015)Simulation and the Monte Carlo Method (2016)A mathematical theory of evidence (1977)Stochastic Finite Elements: A Spectral Approach (1991)Adaptive kriging-based efficient reliability method for structural systems with multiple failure modes and mixed variables (2019)Modeling, analysis, and optimization under uncertainties: a review (2021)Sensitivity Analysis for Importance Assessment (2002)A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters (2021)
this work references cited by second hop

References

  • 2011A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing810 cited
  • 1998Hierarchical Bayesian space-time models413 cited
  • 2006Validation and error estimation of computational models162 cited
  • 2017A new method for model validation with multivariate output24 cited
  • 2015Model validation and calibration based on component functions of model output14 cited
  • 2008A top-down approach to calibration, validation, uncertainty quantification and predictive accuracy assessment14 cited

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