Hello,
I have a general question about regression-based PCE model building: are there any general guidelines on how to select the type of experimental sample (e.g. LHS, Sobol, Monte-Carlo, etc.) used when constructing a PCE model?
Some more detailed questions:
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Is the efficiency of a certain experimental sample type very application-dependent?
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Does it depend on the scale of the considered input variability (e.g. meta-modeling over large input variation domains vs uncertainty quantification)?
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Does it depend on the type of PCE coefficient calculation method (OLS, sparse methods (LARS, OMP, SP, etc.))?
I would be grateful for any insight on this topic, thank you.