<div dir="ltr">I've seen a few threads in this direction:<div><br></div><div>See Sanjukta Bhowmick's work on combining machine learning with PETSc to start: <a href="http://cs.unomaha.edu/~bhowmick/Blog/Entries/2010/9/12_Solvers_for_Large_Sparse_Linear_Systems.html">http://cs.unomaha.edu/~bhowmick/Blog/Entries/2010/9/12_Solvers_for_Large_Sparse_Linear_Systems.html</a></div>
<div><br></div><div>HYPRE has something along the lines of this as well, but I have not seen any promising results.</div><div><br></div><div>Don't forget that even slightly different problems can have wildly different convergence properties, you want a solver that is both fast and robust to changes in your input parameters.</div>
<div><br></div><div>A<br><br><div class="gmail_quote">On Mon, Feb 14, 2011 at 1:27 PM, Michel Cancelliere <span dir="ltr"><<a href="mailto:fernandez858@gmail.com">fernandez858@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
Dear users,<div><br></div><div>I've implemented a simple hydrocarbon reservoir simulator using PETSc, the simulator is used inside an iterative loop in which thousand of simulations are run with different input parameters(In order to calibrate the properties of the model). I would like to use those iterations to tuneup the parameters of the solver (precoditioner,type of linear solver, restart, etc...), Have someone working with that?, Do you know some papers where I can some information about that?</div>
<div><br></div><div>Thank you for your time,</div><div><br></div><font color="#888888"><div>Michel</div>
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