An adaptive least-squares global sensitivity method and application to a plasma-coupled combustion prediction with parametric correlation

DOI10.1016/j.jcp.2018.01.042zbMath1422.65027OpenAlexW2790307289MaRDI QIDQ1637558

Jonathan B. Freund, Luca Massa, Kunkun Tang, Jonathan M. Wang

Publication date: 8 June 2018

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2018.01.042

zbMATH Keywords

dimension reductionadaptive ANOVAcorrelated inputscovariance-based sensitivity analysisplasma-coupled turbulent combustionpolynomial dimensional decomposition

Mathematics Subject Classification ID

Related Items (1)

Adaptive sparse polynomial dimensional decomposition for derivative-based sensitivity

Uses Software

• GRI-Mech 3.0

Cites Work

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• Sensitivity analysis: a review of recent advances
• Robust design optimization by polynomial dimensional decomposition
• Adaptive ANOVA decomposition of stochastic incompressible and compressible flows
• Adaptive-sparse polynomial dimensional decomposition methods for high-dimensional stochastic computing
• A new derivative based importance criterion for groups of variables and its link with the global sensitivity indices
• Accuracy of the simultaneous-approximation-term boundary condition for time-dependent problems
• Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations
• General foundations of high-dimensional model representations
• Making best use of model evaluations to compute sensitivity indices
• Efficient input-output model representations
• Coherence motivated sampling and convergence analysis of least squares polynomial chaos regression
• A stochastic projection method for fluid flow. II: Random process
• Estimation of global sensitivity indices for models with dependent variables
• Generalized Hoeffding-Sobol decomposition for dependent variables -- application to sensitivity analysis
• Least squares polynomial chaos expansion: a review of sampling strategies
• Analysis of discrete least squares on multivariate polynomial spaces with evaluations at low-discrepancy point sets
• Weighted discrete least-squares polynomial approximation using randomized quadratures
• Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluid simulation
• Uncertainty quantification of limit-cycle oscillations
• Handbook of Uncertainty Quantification
• A Generalized ANOVA Dimensional Decomposition for Dependent Probability Measures
• On Discrete Least-Squares Projection in Unbounded Domain with Random Evaluations and its Application to Parametric Uncertainty Quantification
• Multivariate Discrete Least-Squares Approximations with a New Type of Collocation Grid
• Sensitivity analysis using anchored ANOVA expansion and high-order moments computation
• A Christoffel function weighted least squares algorithm for collocation approximations
• ORTHOGONAL POLYNOMIAL EXPANSIONS FOR SOLVING RANDOM EIGENVALUE PROBLEMS
• A polynomial dimensional decomposition for stochastic computing
• Computational Statistics
• Spectral Methods for Uncertainty Quantification
• Uncertainty quantification in chemical systems
• Uniformly distributed sequences with an additional uniform property
• The vorticity jump across a shock in a non-uniform flow
• On the Relative Importance of Input Factors in Mathematical Models
• The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
• Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach
• Sensitivity Analysis in Practice
• Global Sensitivity Analysis
• On Global Sensitivity Indices: Monte Carlo Estimates Affected by Random Errors
• Stochastic Collocation on Unstructured Multivariate Meshes
• Monte Carlo estimators for small sensitivity indices
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

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