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Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models
16 references·0 citing·34 second hop
Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models (2022)Uncertainty Quantification Framework for …An uncertainty quantification framework for multiscale parametrically homogenized constitutive models (PHCMs) of polycrystalline Ti alloys (2021)An uncertainty quantification…Variance-based sensitivity analysis: The quest for better estimators and designs between explorativity and economy (2020)Variance-based sensitivity an…Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 User's Manual (2020)Dakota, A Multilevel Parallel…Uncertainty Quantification in the Mechanical Response of Crystal Plasticity Simulations (2019)Uncertainty Quantification in…Multivariate Polynomial Chaos Expansions with Dependent Variables (2018)Multivariate Polynomial Chaos…Reduced Wiener Chaos representation of random fields via basis adaptation and projection (2017)Reduced Wiener Chaos represen…Trends in sensitivity analysis practice in the last decade (2016)Trends in sensitivity analysi…Chaospy: An open source tool for designing methods of uncertainty quantification (2015)Chaospy: An open source tool …Uncertainty quantification through the Monte Carlo method in a cloud computing setting (2014)Uncertainty quantification th…On the convergence of generalized polynomial chaos expansions (2011)On the convergence of general…A non-adapted sparse approximation of PDEs with stochastic inputs (2011)A non-adapted sparse approxim…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. 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References

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2017
Reduced Wiener Chaos representation of random fields via basis adaptation and projection
48 cited
  • 2018Multivariate Polynomial Chaos Expansions with Dependent Variables41 cited
  • 2021An uncertainty quantification framework for multiscale parametrically homogenized constitutive models (PHCMs) of polycrystalline Ti alloys31 cited
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