[Numpy-discussion] Interpolation question

Andrea Gavana andrea.gavana at gmail.com
Sun Mar 28 17:40:15 EDT 2010

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 <peridot.faceted at gmail.com> wrote:
>>>> On 27 March 2010 20:24, Andrea Gavana <andrea.gavana at 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.

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.
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