How to bulid PCE model for given data?

Hi everyone,

I am wondering how to build PCE model based on given correlated non-Gaussian data.

For example, I have a data matrix with 30 dimensions and 17520 observations. I am assuming the underlying copula is the Gaussian copula. Then what should I do to build a PCE model based on this data? Should I do the Isoprobabilistic transformation myself? Or will the Uqlab automatically do this for me if I specify my input as following:

iOpts.Marginals = uq_KernelMarginals(solar_power’);
C = corr(solar_power’,‘Type’, ‘Spearman’);
iOpts.Copula.Type = ‘Gaussian’;
iOpts.Copula.RankCorr = C;

Any help or sample code will be appreciated.

Best regards,
Yx Wu

I suggest you start from the example:

Thank you for your reply. But I think this case focuses on useing existing data to replace experimental design, not using exsiting data to define an input object.

For the given input data, I am wondering how will uqlab deal with it inside this toolbox? In my case above, I used kernel estimation to infer the input distribution and I assumed the dependence structure is a Gaussian Copula. Then if I continued with uqlab to build model and PCE metamodel, what will uqlab do? Will it do an isoprobabilistic transformation automatically based on which type of polynomial basis I have assigned?

As far as I know, PCE automatically applies transformations. However, I have noticed that they have made many changes and updates. Therefore, I would suggest setting a breakpoint in the corresponding .m file that you are concerned about to double-check. If you are wondering how UQLab deals with it inside its toolbox, the best way to find out is to go through the code along with the manual.

Got it! Thank you very much.