Hi All, On 28 March 2010 22:14, Pierre GM wrote: > On Mar 28, 2010, at 4:47 PM, Andrea Gavana wrote: >> HI All, >> >> On 28 March 2010 19:22, Robert Kern wrote: >>> On Sun, Mar 28, 2010 at 03:26, Anne Archibald <[email protected]> >>> wrote: >>>> On 27 March 2010 20:24, Andrea Gavana <[email protected]> wrote: >>>>> Hi All, >>>>> >>>>> I have an interpolation problem and I am having some difficulties >>>>> in tackling it. I hope I can explain myself clearly enough. >>>>> >>>>> Basically, I have a whole bunch of 3D fluid flow simulations (close to >>>>> 1000), and they are a result of different combinations of parameters. > >> It seems like this whole interpolation stuff is not working as I >> thought. In particular, considering scalar-valued interpolation (i.e., >> looking at the final oil recovery only and not the time-based oil >> production profile), interpolation with RBFs is giving >> counter-intuitive and meaningless answers. The issues I am seeing are >> basically these: > > Which is hardly surprising: you're working with a physical process, you must > have some constraints on your parameters (whether dependence between > parameters, bounds on the estimates...) that are not taken into account by > the interpolation scheme you're using. So, it's back to the drawing board.
The curious thing is, when using the rbf interpolated function to find a new approximation, I am not giving RBFs input values that are outside the bounds of the existing parameters. Either they are exactly the same as the input ones (for a single simulation), or they are slightly different but always inside the bounds. I always thought that, at least for the same input > What are you actually trying to achieve ? Find the best estimates of your 10 > parameters to match an observed production timeline ? Find a space for your > 10 parameters that gives some realistic production ? > Assuming that your 10 parameters are actually independent, did you run > 1000**10 simulations to test all the possible combinations? Probably not, so > you could try using a coarser interval between min and max values for each > parameters (say, 10 ?) and check the combos... Or you could try to decrease > the number of parameters by finding the ones that have more influence on the > final outcome and dropping the others. A different problem all together... > My point is: don't be discouraged by the weird results you're getting: it's > probably because you're not using the right approach yet. > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > -- Andrea. "Imagination Is The Only Weapon In The War Against Reality." http://xoomer.alice.it/infinity77/ ==> Never *EVER* use RemovalGroup for your house removal. You'll regret it forever. http://thedoomedcity.blogspot.com/2010/03/removal-group-nightmare.html <== _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
