Hello Marc,

Scilab is well fitted for this kind of problem if you use the right tools: we 
now have the sundials ode solvers and the very robust Ipopt, both working with 
Scilab or compiled gateways. I have already used these features for big 
identification problems, so fell free to contact me directly.

Best regards,

S.

> Le 27 août 2024 à 19:14, Marc Bletry <marc.ble...@univ-tours.fr> a écrit :
>
> Hello,
>
> I have a problem under the form of a set of 2 ODE that includes 6 free 
> parameters. I need to optimize these parameters based on experimental 
> results. So far, basically, I have implemented a C++ object that solves the 
> problem (using RK4 method), can interpolate these results, can load an 
> experimental file and determine the distance between model and results using 
> the interpolated function. All this is in one big fat class. Its constructor 
> takes 2 filenames: one for the experimental results and the other one for 
> time parameters driving the system (strain and strain rate, to be more 
> precise).
>
> Now, I am looking for a method to adjust my parameters (which differ greatly 
> in magnitude : some are of the order of 1e-28, others 0.02 and others 1e9 or 
> even 1e13). I was wondering whether it would be possible to use Scilab's 
> optim to do this, but I can't find an example using C++ classes : only C and 
> Fortran 90. Would anyone know if there is a way to do this ? I don't want to 
> have to reload the experimental files at each iteration : that would take 
> forever, so it seems to me I need an object to store this... or maybe there 
> are solutions in C, but I'm really not familiar with that language...
>
> Thanks a lot !
>
> Marc
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