I am currently building with openturns chaos polynomial and kriging metamodels
I have 20 input parameters and 3 outputs (scalars)
I build metamodel for each output independantly
with a sample of 200 computations (LHS)
I compute predictivity factor Q2 based on a test sample of 20 points
I could do more simulations if needed
for the first output it’s very good, for both type of metamodels (Q2 of 0.97 - 0.99)
but for the 2 other outputs it’s very bad (Q2 < 0.5, sometimes Q2 near 0 and < 0)
I have the feeling that these 2 results could be discontinuous (and physically it’s the case I believe)
one of them is zero in many cases (when geometric parameters are not too bad) and sometimes in some geometric configuration this result is not zero
what could I do in this context ? a better choice of some algorithm used by metamodel building process ?
Thanks in advance for your help !