Hello,
I am using the Active Learning Reliability (ALR) framework in UQLab and would like to better understand how the candidate populations are handled internally during the enrichment process.
My understanding is that the learning function (e.g., the U-function) is evaluated on a candidate population generated by the reliability algorithm at each iteration. However, I am not completely clear about whether these samples are regenerated or reused throughout the active learning procedure.
More specifically:
• Is the candidate population used for enrichment regenerated after each surrogate update, or is it kept fixed during the AL process?
• When ALR is combined with Monte Carlo Simulation (MCS), is the population used for estimating Pf regenerated at each iteration, or reused?
• Is there any theoretical drawback to using a fixed candidate population (while removing already-selected enrichment points) instead of regenerating the candidate set at every iteration?
My interest comes from an application involving highly non-smooth limit-state functions, where the handling of candidate populations appears to have a noticeable influence on the convergence behavior of the active learning process.
Thank you very much for your help.