 #1
Frank Einstein
 163
 1
Hello everyone. I am trying to construct a functioning version of randomfields (specifically 2D_karhunen_loeve_identification_example.py) in Matlab. For that, I have to calculate the KarhunenLoève expansion of 2D data, since this is what it says in the documentation. I also have some sample data to test my results.
I have a matrix of size 144*7, being 144 the number of points, 5 the number of random variables and the first two, the X and Y points of each value. If I ignore the first two rows and calculate
the resulting eigenvectors and eigenvalues don't look at all like the ones of the sample data. I was wondering if this might happen because I am ignoring the grid positions at the time of calculating the covariance matrix.
Best regards.
Frank.
E. G. I will attach the stochastic realizations I have received and what I am expected to get
I have a matrix of size 144*7, being 144 the number of points, 5 the number of random variables and the first two, the X and Y points of each value. If I ignore the first two rows and calculate
Eigenpair:
[evec, eval]=eig(cov(realizations));
Best regards.
Frank.
E. G. I will attach the stochastic realizations I have received and what I am expected to get
Attachments

2D_sample_paths.txt25 KB · Views: 90

2D_identification_eigensolution_0.png15.7 KB · Views: 74

2D_identification_eigensolution_1.png14.8 KB · Views: 72

2D_identification_eigensolution_2.png16.3 KB · Views: 69

2D_identification_eigensolution_3.png15.8 KB · Views: 68

2D_identification_eigensolution_4.png15.4 KB · Views: 75