ShampineCollocationInit: For Index-1 DAEs implicit DAEs and semi-explicit DAEs in mass matrix form.Keeps the differential variables constant. BrownFullBasicInit: For Index-1 DAEs implicit DAEs and semi-explicit DAEs in mass matrix form.Full List of Methods Initialization Schemesįor all OrdinaryDiffEq.jl methods, an initialization scheme can be set with a common keyword argument initializealg. If Julia types are required, currently DFBDF is the best method but still needs more optimizations. If the problem cannot be defined in mass matrix form, the recommended method for performance is IDA from the Sundials.jl package if you are solving problems with Float64. Non-constant mass matrices are not directly supported: users are advised to transform their problem through substitution to a DAE with constant mass matrices. Another choice at high accuracy is Rodas5P and RadauIIA5. Rosenbrock23 is better for low accuracy (error tolerance <1e-4) and Rodas4 is better for high accuracy. Edit on GitHub DAE Solvers Recommended Methodsįor medium to low accuracy small numbers of DAEs in constant mass matrix form, the Rosenbrock23 and Rodas4 methods are good choices which will get good efficiency if the mass matrix is constant. Reduced Compile Time, Optimizing Runtime, and Low Dependency Usage.Specifying (Non)Linear Solvers and Preconditioners.Dynamical, Hamiltonian, and 2nd Order ODE Solvers.Non-autonomous Linear ODE / Lie Group ODE Solvers.Dynamical, Hamiltonian and 2nd Order ODE Problems.Non-autonomous Linear ODE / Lie Group Problems.Common Solver Options (Solve Keyword Arguments).Solving the heat equation with diffusion-implicit time-stepping.An Implicit/Explicit CUDA-Accelerated Solver for the 2D Beeler-Reuter Model.Finding Maxima and Minima of ODEs Solutions.Code Optimization for Differential Equations.Getting Started with Differential Equations in Julia.DifferentialEquations.jl: Efficient Differential Equation Solving in Julia.
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