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For more information on using the solvers from this\npackage, see the [DifferentialEquations.jl documentation](https://docs.sciml.ai/DiffEqDocs/stable/).\n\n## Installation\n\nA 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](https://sourceforge.net/projects/mingw-w64/). Then add the path to your environment variables. An example path is:\n\n```\nC:\\Program Files\\mingw-w64\\x86_64-6.1.0-posix-seh-rt_v5-rev0\\mingw64\\bin\n```\n\nNote that it is required that you add ODEInterface.jl as well:\n\n```julia\n]add ODEInterface\n```\n\nOtherwise you may have issues instantiating the solvers.\n\n## Common API Usage\n\nThis library adds the common interface to ODEInterface.jl's solvers. [See the DifferentialEquations.jl documentation for details on the interface](https://docs.sciml.ai/DiffEqDocs/stable/). Following the Lorenz example from [the ODE tutorial](https://docs.sciml.ai/DiffEqDocs/stable/tutorials/ode_example/), we can solve this using `dopri5` via the following:\n\n```julia\nusing ODEInterface, ODEInterfaceDiffEq\nODEInterface.loadODESolvers()\nfunction lorenz(du,u,p,t)\n du[1] = 10.0(u[2]-u[1])\n du[2] = u[1]*(28.0-u[3]) - u[2]\n du[3] = u[1]*u[2] - (8/3)*u[3]\nend\nu0 = [1.0;0.0;0.0]\ntspan = (0.0,100.0)\nprob = ODEProblem(lorenz,u0,tspan)\nsol = solve(prob,dopri5(),abstol=1e-4)\nusing Plots; plot(sol,vars=(1,2,3))\n```\n\nThe options available in `solve` are documented [at the common solver options page](https://docs.sciml.ai/DiffEqDocs/stable/basics/common_solver_opts/). The available methods are documented [at the ODE solvers page](https://docs.sciml.ai/DiffEqDocs/stable/solvers/ode_solve/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsciml%2Fodeinterfacediffeq.jl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsciml%2Fodeinterfacediffeq.jl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsciml%2Fodeinterfacediffeq.jl/lists"}