🚀 go-pugleaf

I'm trying to fit a model to historical data, and keep hitting
limitations and problems with various approaches/packages.
This doesn't seem to be too weird of a problem, but I'm getting frustrated.
I'm not a statistics guru, so I could be (probably am) wrong.

The problem, I have several samples of data in x and y that I need
to fit a surface/model to. X and Y are independent. For each sample, 
x ranges from 30 to 60 by increments of 2, while y goes from 130 to
260 by increments of 5, I have values for each point of this matrix,
although the border/outer edge data values are unreliable.  When plotted,
the surface created by the seperate data samples looks like a fairly
"steep" poisson density function (tall hump in the middle). I've been
unable to find a reference/package for this type of a fit. I've found 
linear/quadratic surface algorithms, but this is exponential. I've found
exponential curve algorithms, but no surfaces except where y is a dependent
variable ( if I am understanding things correctly, I'm refering to ODRPACK
here). 

I'm guessing a model of the form (roughly):

                             F G                       M N
                B      D(1-Ex )          I       K(1-Ly )
     f(x,y) = Ax  *  Ce             +  Hy  *  Je             + Pxy


Any ideas/pointers/references would be GREATLY appreciated!

thanks

Steve = steve@bouteloua.cfnr.colostate.edu