SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Global sensitivity analysis via multi-fidelity polynomial chaos expansion
16 references·16 citing·29 second hopsampled
Global sensitivity analysis via multi-fidelity polynomial chaos expansion (2017)Global sensitivity analysis via multi-fid…AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)AK-MCS: An active learning re…Adaptive sparse polynomial chaos expansion based on least angle regression (2010)Adaptive sparse polynomial ch…Polynomial chaos expansion for sensitivity analysis (2008)Polynomial chaos expansion fo…Multilevel Monte Carlo Path Simulation (2008)Multilevel Monte Carlo Path S…Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions (2008)Efficient Global Reliability …Global sensitivity analysis using polynomial chaos expansions (2007)Global sensitivity analysis u…High-Order Collocation Methods for Differential Equations with Random Inputs (2005)High-Order Collocation Method…Application of kriging method to structural reliability problems (2004)Application of kriging method…Modeling uncertainty in flow simulations via generalized polynomial chaos (2003)Modeling uncertainty in flow …The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)The Wiener--Askey Polynomial …Reliability-based structural optimization using neural networks and Monte Carlo simulation (2002)Reliability-based structural …A Stochastic Projection Method for Fluid Flow (2002)A Stochastic Projection Metho…Importance measures in global sensitivity analysis of nonlinear models (1996)Importance measures in global…STATISTICS FOR SPATIAL DATA (1992)STATISTICS FOR SPATIAL DATAStochastic Finite Elements: A Spectral Approach (1991)Stochastic Finite Elements: A…Design and Analysis of Computer Experiments (1989)Design and Analysis of Comput…MULTI-FIDELITY MACHINE LEARNING FOR UNCERTAINTY QUANTIFICATION AND OPTIMIZATION (2024)MULTI-FIDELITY MACHINE LEARNI…Learnable quantile polynomial chaos expansion: An uncertainty quantification method for interval reliability analysis (2024)Learnable quantile polynomial…Global sensitivity analysis of design variables for porous hydrostatic gas bearings considering uncertainty (2024)Global sensitivity analysis o…AMFGP: An active learning reliability analysis method based on multi-fidelity Gaussian process surrogate model (2024)AMFGP: An active learning rel…Uncertainty quantification and global sensitivity analysis of composite wind turbine blades (2022)Uncertainty quantification an…Deep adaptive arbitrary polynomial chaos expansion: A mini-data-driven semi-supervised method for uncertainty quantification (2022)Deep adaptive arbitrary polyn…Approach for the structural reliability analysis by the modified sensitivity model based on response surface function - Kriging model (2022)Approach for the structural r…An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis (2022)An efficient multi-fidelity K…Uncertainty and multi-criteria global sensitivity analysis of structural systems using acceleration algorithm and sparse polynomial chaos expansion (2021)Uncertainty and multi-criteri…Multi-fidelity uncertainty quantification of film cooling flow under random operational and geometrical conditions (2020)Multi-fidelity uncertainty qu…Active learning polynomial chaos expansion for reliability analysis by maximizing expected indicator function prediction error (2020)Active learning polynomial ch…Regression-based sparse polynomial chaos for uncertainty quantification of subsurface flow models (2019)Regression-based sparse polyn…Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression (2019)Multi-level multi-fidelity sp…Multi-fidelity uncertainty propagation using polynomial chaos and Gaussian process modeling (2019)Multi-fidelity uncertainty pr…Efficient methods by active learning Kriging coupled with variance reduction based sampling methods for time-dependent failure probability (2019)Efficient methods by active l…Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices (2018)Multifidelity Monte Carlo Est…Structural Reliability Methods (1996)Kriging metamodeling in simulation: A review (2007)A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models (2012)Metamodel-based importance sampling for structural reliability analysis (2013)LIF: A new Kriging based learning function and its application to structural reliability analysis (2016)Statistics for Experimenters (1979)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Handbook on Constructing Composite Indicators (2005)An effective screening design for sensitivity analysis of large models (2007)Sensitivity analysis of environmental models: A systematic review with practical workflow (2016)A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output (1999)Large Sample Properties of Simulations Using Latin Hypercube Sampling (1987)A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data (2007)A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data (2008)A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options (1993)Monte Carlo Methods in Financial Engineering (2003)Multi-level Monte Carlo Finite Element method for elliptic PDEs with stochastic coefficients (2011)Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients (2012)Numerical solution of stochastic differential equations (2016)Optimal Model Management for Multifidelity Monte Carlo Estimation (2016)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index (2009)Taking the Human Out of the Loop: A Review of Bayesian Optimization (2015)Efficient computation of global sensitivity indices using sparse polynomial chaos expansions (2010)UQLab: A Framework for Uncertainty Quantification in Matlab (2014)POLYNOMIAL-CHAOS-BASED KRIGING (2015)Local Indicators of Spatial Association—LISA (1995)Methods to account for spatial autocorrelation in the analysis of species distributional data: a review (2007)Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure (2016)
this work references cited by second hop· second hop is a sample of the most-cited connections

References

  • 1992STATISTICS FOR SPATIAL DATA9,140 cited
  • 1989Design and Analysis of Computer Experiments6,958 cited
  • 1991Stochastic Finite Elements: A Spectral Approach5,753 cited
  • 2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations4,821 cited
  • 2007Global sensitivity analysis using polynomial chaos expansions2,547 cited
  • 2011AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation2,048 cited
  • 1996Importance measures in global sensitivity analysis of nonlinear models2,014 cited
  • 2005High-Order Collocation Methods for Differential Equations with Random Inputs1,522 cited
  • 2010Adaptive sparse polynomial chaos expansion based on least angle regression1,507 cited
  • 2008Multilevel Monte Carlo Path Simulation1,436 cited
  • 2003Modeling uncertainty in flow simulations via generalized polynomial chaos1,428 cited
  • 2008Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions1,141 cited
  • 2008Polynomial chaos expansion for sensitivity analysis747 cited
  • 2004Application of kriging method to structural reliability problems709 cited
  • 2002Reliability-based structural optimization using neural networks and Monte Carlo simulation

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

516 cited
  • 2002A Stochastic Projection Method for Fluid Flow503 cited
  • Cited by

    • 2022An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis79 cited
    • 2018Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices78 cited
    • 2019Efficient methods by active learning Kriging coupled with variance reduction based sampling methods for time-dependent failure probability73 cited
    • 2022Uncertainty quantification and global sensitivity analysis of composite wind turbine blades61 cited
    • 2022Deep adaptive arbitrary polynomial chaos expansion: A mini-data-driven semi-supervised method for uncertainty quantification44 cited
    • 2019Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression44 cited
    • 2024AMFGP: An active learning reliability analysis method based on multi-fidelity Gaussian process surrogate model36 cited
    • 2019Multi-fidelity uncertainty propagation using polynomial chaos and Gaussian process modeling27 cited
    • 2019Regression-based sparse polynomial chaos for uncertainty quantification of subsurface flow models26 cited
    • 2021Uncertainty and multi-criteria global sensitivity analysis of structural systems using acceleration algorithm and sparse polynomial chaos expansion25 cited
    • 2024Learnable quantile polynomial chaos expansion: An uncertainty quantification method for interval reliability analysis25 cited
    • 2020Active learning polynomial chaos expansion for reliability analysis by maximizing expected indicator function prediction error20 cited
    • 2024MULTI-FIDELITY MACHINE LEARNING FOR UNCERTAINTY QUANTIFICATION AND OPTIMIZATION15 cited
    • 2022Approach for the structural reliability analysis by the modified sensitivity model based on response surface function - Kriging model15 cited
    • 2020Multi-fidelity uncertainty quantification of film cooling flow under random operational and geometrical conditions14 cited
    • 2024Global sensitivity analysis of design variables for porous hydrostatic gas bearings considering uncertainty14 cited