[Numpy-discussion] Interpolation question
Pierre GM
pgmdevlist@gmail....
Sun Mar 28 16:14:50 CDT 2010
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 <peridot.faceted@gmail.com> wrote:
>>> On 27 March 2010 20:24, Andrea Gavana <andrea.gavana@gmail.com> 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.
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.
More information about the NumPy-Discussion
mailing list