Dear OT users,
I am working right now on a UQ workflow with a numerical model taking into inputs 19 uncertain variables and computing out 3 quantities of interest.
For the UQ analysis (sobol sensitivity, forward propagation, etc.) I construct a Kriging model.
The trouble is that for 2 of the QOIs, the validation of the surrogate (computed on an independent validation set of 60 points) is still very bad, even as I increase the size of the DOE.
For instance i attach here the figures of the qq plots for each QOI (Y0,Y1,Y2), considering a Kriging constructed on a DOE of 4000 points sampled with LHS, according to uniform distribution. Note that the Q2 scores are worst with smaller DOE sizes. It means that when I increase the size the of the DOE, the validation is getting better but it is very weak enhancement.
qqPlotKriging_qoi_0.pdf (196.7 KB)
qqPlotKriging_qoi_1.pdf (196.7 KB)
qqPlotKriging_qoi_2.pdf (196.8 KB)
Would you have any advice or tips to enhance the surrogate model accuracy with respect with the real model ? Or am I just stuck in the curse of dimensionality with so many inputs ?
An other question is about the Kriging algorithm complexity, it takes very long time to construct the Kriging with so much points, but I assume it is expected considering the size of the DOE.
Thanks by advance for your help in this topic, and I look forward to discuss these issues with the community.
Best regards,
Elie