MethodOfLines.jl
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SciML
Automatic Finite Difference PDE solving with Julia SciML
Adds metadata and solution interfaces This branch is based off #151, as it was started to aid in solution retrieval.
Implements 2nd order time derivatives, still some issues to work out but progressing well. fixes #150 fixes #52 fixes #76
Right now it returns each of the pieces of the solution, but it would be nice to get the observed variable handling working so that `sol[u]` returns the whole (shaped?)...
Previously corners were set to 0, Here they are extrapolated. fixes #148
Hi all, I got errors when converting PDE problem to ODE problem by using "discretize". Could someone please help? Here are the codes: using ModelingToolkit, DomainSets, MethodOfLines, OrdinaryDiffEq @parameters t,...
Hi, I want to see if this package can solve a system of 2D compressible ideal magnetohydrodynamic equations in the X-Z plane. ## Problem Description The original equations are $$...
Trying to run this, ``` `using ModelingToolkit, MethodOfLines, OrdinaryDiffEq, DomainSets, LinearAlgebra, Dierckx, NonlinearSolve theta = 0:pi/20:pi xs = cos.(theta) ys = sin.(theta) points = [xs ys] fchapBA = .1 fchapBF...
At present corners are zeroed, better would be to interpolate/extrapolate them. Easy in 2 dimensions, much harder to do correctly in higher dimensions.
Moving towards implementing a `DiscretizedVariable` with a smart recursive `getindex` and custom dict based indexing like `Dict([x=>i, z=>k])` would allow for sampling whole expressions at once, and much greater flexibility,...