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MultivariateOrthogonalPolynomials

Supports approximating functions and solving differential equations on disks in ApproxFun

Readme

MultivariateOrthogonalPolynomials.jl

Build Status

This is an experimental package to add support for multivariate orthogonal polynomials on disks, spheres, triangles, and other simple geometries to ApproxFun.jl. At the moment it primarily supports triangles. For example, we can solve variable coefficient Helmholtz on the triangle with zero Dirichlet conditions as follows:

using ApproxFun, MultivariateOrthogonalPolynomials
x,y = Fun(Triangle())
Δ = Laplacian() : TriangleWeight(1.0,1.0,1.0,JacobiTriangle(1.0,1.0,1.0))
V = x*y^2
L = Δ + 200^2*V
u = \(L, ones(Triangle()); tolerance=1E-5)

See the examples folder for more examples, including non-zero Dirichlet conditions, Neumann conditions, and piecing together multiple triangles. In particular, the examples from Olver, Townsend & Vasil 2019.

This code relies on Slevinsky's FastTransforms C library for calculating transforms between values and coefficients. At the moment the path to the compiled FastTransforms library is hard coded in c_transforms.jl.

References

S. Olver, A. Townsend & G.M. Vasil (2019), A sparse spectral method on triangles, arXiv:1902.04 S. Olver & Y. Xuan (2019), Orthogonal polynomials in and on a quadratic surface of revolution, arXiv:1906.12305 G.M. Vasil, K.J. Burns, D. Lecoanet, S. Olver, B.P. Brown & J.S. Oishi (2016), Tensor calculus in polar coordinates using Jacobi polynomials, J. Comp. Phys., 325: 53–73

First Commit

09/06/2015

Last Touched

1 day ago

Commits

177 commits

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