Pages that link to "Item:Q1691892"
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The following pages link to Nested polynomial trends for the improvement of Gaussian process-based predictors (Q1691892):
Displaying 10 items.
- A new surrogate modeling technique combining Kriging and polynomial chaos expansions - application to uncertainty analysis in computational dosimetry (Q729119) (← links)
- An efficient methodology for modeling complex computer codes with Gaussian processes (Q1023832) (← links)
- Systems of Gaussian process models for directed chains of solvers (Q1988026) (← links)
- Nested aggregation of experts using inducing points for approximated Gaussian process regression (Q2163216) (← links)
- Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework (Q2184398) (← links)
- An efficient dimension reduction for the Gaussian process emulation of two nested codes with functional outputs (Q2203403) (← links)
- Efficient sequential experimental design for surrogate modeling of nested codes (Q4967799) (← links)
- Gaussian Process Regression on Nested Spaces (Q6109172) (← links)
- Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of helium on graphite substrate (Q6198152) (← links)
- Transient anisotropic kernel for probabilistic learning on manifolds (Q6643613) (← links)
