ODE.jl
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SciML
Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML). Deprecated: Use DifferentialEquations.jl instead.
For some problems, it is interesting to run the solver until something happens - specified by (part of) the solution at that point. In those cases, it can be hard...
DiffEqBenchmarks notes that `ode23s` seems to diverge on very standard test problems: https://github.com/JuliaDiffEq/DiffEqBenchmarks.jl/blob/master/StiffODE/VanDerPol.ipynb There might be a correctness issue with it.
I've noticed ode4 can get extremely slow compared to ode45 with the same number of steps or say a hand-written rk4 solver. I tracked this down mostly due to a...
A common dilemma in Julia, is what to do when the user provides integers to a function where they probably want do do the calculation in floating point. Many users...
Hi, I have been playing around with iterator version of `oderkf` and came up with my own implementation. My idea was what follows: make `oderkf(F,x0,tspan::Vector)` behave as previously and add...
This PR adds two functions, `verlet_fixed` and `verlet_hh2`, which implement the velocity Verlet method with fixed and adaptive time-steps, respectively. The latter uses an estimate of the error based on...
Hi, I have tried to work out the component-wise relative and absolute tolerances for the non-stiff solvers based on 'Solving Ordinary Differential Equations I by Hairer and Wanner' and component-wise...
There are three major ways of calculating the time-dependent sensitivity of a system of differential equations: - Automatic Differentiation - Finite Differences - Sensitivity Equations Unlike FD or AD, sensitivity...
I can never remember the order of the arguments, and `?ode45` is not particularly helpful.
