MultivariateOrthogonalPolynomials.jl
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Supports approximating functions and solving differential equations on various higher dimensional domains such as disks and triangles
The examples [here](https://github.com/JuliaApproximation/MultivariateOrthogonalPolynomials.jl/blob/master/examples/triangleexamples.jl) fail (below is Lines 1-12 from that file) ```julia julia> using ApproxFun, MultivariateOrthogonalPolynomials, BlockArrays, SpecialFunctions, FillArrays, Plots import ApproxFun: blockbandwidths, Vec, PiecewiseSegment import MultivariateOrthogonalPolynomials: DirichletTriangle S =...
In response to #176, this PR adds an extra check that all the example scripts actually run by simply including them one at a time inside the tests. Currently, these...
This pull request changes the compat entry for the `FastTransforms` package from `0.15.11` to `0.15.11, 0.16`. This keeps the compat entries for earlier versions. Note: I have not tested your...
I managed to implement the partial derivatives with respect to y. The following code now runs: `using MultivariateOrthogonalPolynomials W = Weighted(Zernike(1)) Δ = Laplacian(axes(W,1)) ∂ʸ = PartialDerivative{2}(axes(W,1)) Z⁰ = Zernike(0)...
There is some strange behavior when expanding functions on triangles, apparently when it comes to globally defined variables? Here is a minimal example: ```julia julia> using MultivariateOrthogonalPolynomials julia> T =...
@dlfivefifty The lowering `Zernike(0,1) \ Weighted(Zernike(0,1))` is a factor of 2 off. This also seems to be confirmed in the tests..? https://github.com/JuliaApproximation/MultivariateOrthogonalPolynomials.jl/blob/cb0fb241eecc49fbdb873a5cd2b7de098897d355/test/test_disk.jl#L303 Is there a reason for this?
Am I correct in believing that the Laplacian and edge restriction operators on the triangle have not been implemented in the ClassicalOPs version? I distinctly remember that functionality working in...
These look like `P_{n,k}(x,y) = U_n(x * cos(k*pi/(n+1)) + y * sin(k*pi/(n+1)))`, orthogonal on the unweighted unit disk. Sparse differentiation is trivial (using ultraspherical generalizations), but the Jacobi operators are...