Pages that link to "Item:Q2194350"

The following pages link to Global sensitivity analysis: a Bayesian learning based polynomial chaos approach (Q2194350):

Displaying 11 items.

• Global sensitivity analysis through polynomial chaos expansion of a basin-scale geochemical compaction model (Q509783) (← links)
• Modified GMDH-NN algorithm and its application for global sensitivity analysis (Q1694635) (← links)
• On the use of sparse Bayesian learning-based polynomial chaos expansion for global reliability sensitivity analysis (Q2087515) (← links)
• Global sensitivity analysis for multivariate outputs using polynomial chaos-based surrogate models (Q2174747) (← links)
• On the use of derivatives in the polynomial chaos based global sensitivity and uncertainty analysis applied to the distributed parameter models (Q2214566) (← links)
• Global sensitivity analysis using support vector regression (Q2294004) (← links)
• Bayesian sparse polynomial chaos expansion for global sensitivity analysis (Q2309099) (← links)
• Global sensitivity analysis based on high-dimensional sparse surrogate construction (Q2363869) (← links)
• Optimal sparse polynomial chaos expansion for arbitrary probability distribution and its application on global sensitivity analysis (Q2674097) (← links)
• An analytical structural global sensitivity analysis method based on direct integral (Q4990761) (← links)
• Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark (Q4995117) (← links)