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Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces
4 references·16 citing·37 second hop
Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces (2014)Active Subspace Methods in Theory and Pra…Global Sensitivity Analysis: The Primer by Andrea Saltelli, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni, Debora Gatelli, Michaela Saisana, Stefano Tarantola (2008)Global Sensitivity Analysis: …Global Sensitivity Analysis. The Primer (2007)Global Sensitivity Analysis. …Stochastic spectral methods for efficient Bayesian solution of inverse problems (2006)Stochastic spectral methods f…Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations (2004)Galerkin Finite Element Appro…Efficient reliability analysis based on deep learning-enhanced surrogate modelling and probability density evolution method (2021)Efficient reliability analysi…Surrogate-assisted global sensitivity analysis: an overview (2020)Surrogate-assisted global sen…Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square (2020)Surrogate modeling of high-di…Surrogate modeling immersed probability density evolution method for structural reliability analysis in high dimensions (2020)Surrogate modeling immersed p…Structural reliability analysis via dimension reduction, adaptive sampling, and Monte Carlo simulation (2020)Structural reliability analys…Review of Geometric Uncertainty Quantification in Gas Turbines (2020)Review of Geometric Uncertain…Polynomial Chaos Expansion for Parametric Problems in Engineering Systems: A Review (2020)Polynomial Chaos Expansion fo…Kernel principal component analysis-based Gaussian process regression modelling for high-dimensional reliability analysis (2020)Kernel principal component an…Fast surrogate modeling using dimensionality reduction in model inputs and field output: Application to additive manufacturing (2020)Fast surrogate modeling using…Structural reliability analysis based on polynomial chaos, Voronoi cells and dimension reduction technique (2019)Structural reliability analys…PLS-based adaptation for efficient PCE representation in high dimensions (2019)PLS-based adaptation for effi…Quantifying kinetic uncertainty in turbulent combustion simulations using active subspaces (2018)Quantifying kinetic uncertain…A multivariate interval approach for inverse uncertainty quantification with limited experimental data (2018)A multivariate interval appro…Global sensitivity metrics from active subspaces (2017)Global sensitivity metrics fr…Exploiting active subspaces to quantify uncertainty in the numerical simulation of the HyShot II scramjet (2015)Exploiting active subspaces t…Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition (2014)Enabling High-Dimensional Hie…Stochastic Finite Elements: A Spectral Approach (1991)The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)Certain trends in uncertainty and sensitivity analysis: An overview of software tools and techniques (2019)Modeling, analysis, and optimization under uncertainties: a review (2021)An adaptive multi-element generalized polynomial chaos method for stochastic differential equations (2005)A Stochastic Collocation Approach to Bayesian Inference in Inverse Problems (2009)Simulation-based optimal Bayesian experimental design for nonlinear systems (2012)Stochastic finite element methods for partial differential equations with random input data (2014)Random vibration and statistical linearization (1990)Verification and Validation in Computational Science and Engineering (1998)Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations (2004)Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics (2008)An Introduction to Computational Stochastic PDEs (2014)Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001)Big-Data Tensor Recovery for High-Dimensional Uncertainty Quantification of Process Variations (2016)Multidimensional Uncertainty Quantification of Microwave/RF Networks Using Linear Regression and Optimal Design of Experiments (2016)Stochastic Collocation With Non-Gaussian Correlated Process Variations: Theory, Algorithms, and Applications (2018)Factorial Sampling Plans for Preliminary 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based on Gaussian‐process metamodelling for complex biomechanical problems (2022)Simulation and the Monte Carlo Method (2016)An expected integrated error reduction function for accelerating Bayesian active learning of failure probability (2022)Parallel adaptive Bayesian quadrature for rare event estimation (2022)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)SALib: An open-source Python library for Sensitivity Analysis (2017)Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles (2011)A simple and efficient method for global sensitivity analysis based on cumulative distribution functions (2015)Weights and importance in composite indicators: Closing the gap (2017)
this work references cited by second hop

References

  • 2007Global Sensitivity Analysis. The Primer6,425 cited
  • 2004Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations869 cited
  • 2008Global Sensitivity Analysis: The Primer by Andrea Saltelli, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni, Debora Gatelli, Michaela Saisana, Stefano Tarantola511 cited
  • 2006Stochastic spectral methods for efficient Bayesian solution of inverse problems471 cited

Cited by

  • 2015Exploiting active subspaces to quantify uncertainty in the numerical simulation of the HyShot II scramjet165 cited
  • 2020Surrogate-assisted global sensitivity analysis: an overview165 cited
  • 2017Global sensitivity metrics from active subspaces158 cited
  • 2014Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition98 cited
  • 2020Polynomial Chaos Expansion for Parametric Problems in Engineering Systems: A Review88 cited
  • 2020Kernel principal component analysis-based Gaussian process regression modelling for high-dimensional reliability analysis85 cited
  • 2018A multivariate interval approach for inverse uncertainty quantification with limited experimental data81 cited
  • 2019PLS-based adaptation for efficient PCE representation in high dimensions66 cited
  • 2020Fast surrogate modeling using dimensionality reduction in model inputs and field output: Application to additive manufacturing

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64 cited
  • 2019Structural reliability analysis based on polynomial chaos, Voronoi cells and dimension reduction technique64 cited
  • 2020Structural reliability analysis via dimension reduction, adaptive sampling, and Monte Carlo simulation64 cited
  • 2020Review of Geometric Uncertainty Quantification in Gas Turbines63 cited
  • 2020Surrogate modeling immersed probability density evolution method for structural reliability analysis in high dimensions59 cited
  • 2018Quantifying kinetic uncertainty in turbulent combustion simulations using active subspaces59 cited
  • 2021Efficient reliability analysis based on deep learning-enhanced surrogate modelling and probability density evolution method58 cited
  • 2020Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square55 cited