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Validation Metrics for Deterministic and Probabilistic Data
12 references·0 citing·38 second hop
Validation Metrics for Deterministic and Probabilistic Data (2018)Validation Metrics for Deterministic and …Virtual model validation of complex multiscale systems: Applications to nonlinear elastostatics (2013)Virtual model validation of c…The Parallel C++ Statistical Library ‘QUESO’: Quantification of Uncertainty for Estimation, Simulation and Optimization (2012)The Parallel C++ Statistical …Toward a Better Understanding of Model Validation Metrics (2011)Toward a Better Understanding…Verification and Validation in Scientific Computing (2010)Verification and Validation i…DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's manual. (2010)DAKOTA : a multilevel paralle…Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Variance based sensitivity an…A systematic approach to model validation based on Bayesian updates and prediction related rejection criteria (2008)A systematic approach to mode…Model validation and predictive capability for the thermal challenge problem (2007)Model validation and predicti…Global Sensitivity Analysis. The Primer (2007)Global Sensitivity Analysis. …A Framework for Validation of Computer Models (2007)A Framework for Validation of…Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models (2004)Sensitivity Analysis in Pract…Verification and Validation in Computational Science and Engineering (1998)Verification and Validation i…Stochastic Finite Elements: A Spectral Approach (1991)Response Surface Methodology: Process and Product Optimization Using Designed Experiments (1996)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Comparison of Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Quantification (2009)Recent Advances in Non-Intrusive Polynomial Chaos and Stochastic Collocation Methods for Uncertainty Analysis and Design (2009)Chaospy: An open source tool for designing methods of uncertainty quantification (2015)Estimation of small failure probabilities in high dimensions by subset simulation (2001)Stochastic Calculus: Applications in Science and Engineering (2002)Adaptive multiscale predictive modelling (2018)Bayesian inference of random fields represented with the Karhunen–Loève expansion (2019)Unified Framework and Survey for Model Verification, Validation and Uncertainty Quantification (2020)Design and Analysis of Computer Experiments (1989)Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences (1994)Calibration, validation, and sensitivity analysis: What's what (2006)Measures of agreement between computation and experiment: Validation metrics (2006)Quantification of Model Uncertainty: Calibration, Model Discrepancy, and Identifiability (2012)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)How to avoid a perfunctory sensitivity analysis (2010)Probability, reliability and statistical methods in engineering design (1999)Probability, Reliability and Statistical Methods in Engineering Design (2001)Verification, validation, and predictive capability in computational engineering and physics (2004)Quantitative model validation techniques: New insights (2012)Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems (2015)Review of statistical model calibration and validation—from the perspective of uncertainty structures (2019)The American society of mechanical engineers (1902)CHAPMAN AND HALL (1985)Empirical Model Building and Response Surfaces (1988)A better understanding of model updating strategies in validating engineering models (2008)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)SALib: An open-source Python library for Sensitivity Analysis (2017)A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing (2011)Imprecise probabilities in engineering analyses (2013)Combustion kinetic model uncertainty quantification, propagation and minimization (2014)Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations (2004)Survey of sampling-based methods for uncertainty and sensitivity analysis (2006)Handbook on Constructing Composite Indicators (2005)Tools for Composite Indicators Building (2005)An effective screening design for sensitivity analysis of large models (2007)
this work references cited by second hop

References

  • 2007Global Sensitivity Analysis. The Primer6,425 cited
  • 2009Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index3,567 cited
  • 1998Verification and Validation in Computational Science and Engineering2,153 cited
  • 2004Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models1,797 cited
  • 2010Verification and Validation in Scientific Computing1,551 cited
  • 2010DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's manual.775 cited
  • 2007A Framework for Validation of Computer Models656 cited
  • 2007Model validation and predictive capability for the thermal challenge problem322 cited
  • 2011Toward a Better Understanding of Model Validation Metrics175 cited
  • 2008A systematic approach to model validation based on Bayesian updates and prediction related rejection criteria76 cited
  • 2012The Parallel C++ Statistical Library ‘QUESO’: Quantification of Uncertainty for Estimation, Simulation and Optimization69 cited
  • 2013Virtual model validation of complex multiscale systems: Applications to nonlinear elastostatics24 cited

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