Adds the common API onto ODEInterface classic Fortran methods for the SciML Scientific Machine Learning organization
Author SciML
8 Stars
Updated Last
12 Months Ago
Started In
December 2016


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This package contains bindings for ODEInterface.jl to allow it to be used with the JuliaDiffEq common interface. For more information on using the solvers from this package, see the DifferentialEquations.jl documentation.


A standard installation on MacOSX and Linux should work. On Windows, you need to install mingw32 compilers and add them to the path. MingW32 can be found here. Then add the path to your environment variables. An example path is:

C:\Program Files\mingw-w64\x86_64-6.1.0-posix-seh-rt_v5-rev0\mingw64\bin

Note that it is required that you add ODEInterface.jl as well;

]add ODEInterface

Otherwise you may have issues instantiating the solvers.

Common API Usage

This library adds the common interface to ODEInterface.jl's solvers. See the DifferentialEquations.jl documentation for details on the interface. Following the Lorenz example from the ODE tutorial, we can solve this using dopri5 via the following:

using ODEInterface, ODEInterfaceDiffEq
function lorenz(du,u,p,t)
 du[1] = 10.0(u[2]-u[1])
 du[2] = u[1]*(28.0-u[3]) - u[2]
 du[3] = u[1]*u[2] - (8/3)*u[3]
u0 = [1.0;0.0;0.0]
tspan = (0.0,100.0)
prob = ODEProblem(lorenz,u0,tspan)
sol = solve(prob,dopri5(),abstol=1e-4)
using Plots; plot(sol,vars=(1,2,3))

The options available in solve are documented at the common solver options page. The available methods are documented at the ODE solvers page.