Hi Mauro, I would like to submit a proposal to work on the ODE.jl package, for the GSoC. From my undergraduate and master thesis I have experience with the Taylor method for solving ODEs (ie., based on Taylor series expansions). This is a variable order, variable step size method, which uses automatic differentiation techniques in order to reach high order integration methods (30th, 40th order) which enable machine-epsilon precision with very competitive speeds. I think the Taylor method is important to include in the ODE.jl package, as it is very versatile and precise.
Besides the utility of the Taylor method for ODEs integration, a DAEs solver can also be implemented using the Taylor models framework. I would be very happy to contribute to the ODE.jl package! Best regards, On Thursday, February 11, 2016 at 7:56:45 AM UTC-6, Mauro wrote: > > > It is desirable to have ode-solvers which are pure Julia. Both to cut > down on dependencies and to allow easy hacking and development. > Further, Sundials.jl will not work with generic Julia datatypes (e.g. I > think Julia sparse matrices are not supported for Jacobians). Thus, > ODE.jl is to stay and to be improved on. > > The currently ongoing work of which I'm aware is: > https://github.com/JuliaLang/ODE.jl/pull/49 > https://github.com/JuliaLang/ODE.jl/pull/72 > > Needed work is: > - more solvers > - a unified code structure/API > - parallelism(?) > > I'll try and update the GSoC description. >
