I’m having trouble reading the formulae, but I guess its a nonlinear
PDE in 3D? Right now the package can only do nonlinear ODEs and linear PDEs
on rectangles and disks. We’ll hopefully eventually extend it to nonlinear
PDEs, and 3D PDEs.
> On 17 Sep 2015, at 11:40 am, Luke Stagner <lstagne...@gmail.com> wrote:
>
> Can this package be used to solve the Grad-Shafranov equation
> <https://en.wikipedia.org/wiki/Grad%E2%80%93Shafranov_equation>?
>
>
> On Wednesday, September 16, 2015 at 3:33:22 PM UTC-7, Sheehan Olver wrote:
>
> ApproxFun is a package for approximating and solving differential equations.
> ApproxFun v0.0.8 Adds (experimental) support for solving nonlinear ODEs,
> using Newton iteration and automatic differentiation. The following example
> solves and plots a singularly perturbed nonlinear two-point boundary value
> problem
>
> x=Fun()
> u0=0.x # The initial guess for Newton iteration
>
> N=u->[u[-1.]-1.,u[1.]+0.5,0.001u''+6*(1-x^2)*u'+u^2-1.]
> u=newton(N,u0)
> ApproxFun.plot(u) # Requires PyPlot or Gadfly
>
>
> Note: previous support for approximating functions on a disk has been moved
> to a separate package:
>
> https://github.com/ApproxFun/DiskFun.jl
> <https://github.com/ApproxFun/DiskFun.jl>
>
> And this will be the last version to support Julia 0.3!
>
