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Contributing

What is currently implemented

CorrectionFactorMatrixMethod.jl provides a matrix-free BEM framework for three-dimensional problems. The following is in place:

  • Core machinery: correction-factor assembly driven by an H2Trees.BlockTree, sparse near-field correction via BlockSparseMatrices.jl, and a LinearMaps.jl-compatible PetrovGalerkinCFMM operator supporting forward, transpose, and adjoint products.
  • BEAST extension (CFMMBEAST): quadrature, collocation, and corrected-kernel assembly for all BEAST IntegralOperator types; specialisations for Helmholtz 3D (V, K, K', W) with Lagrange C0D1 basis functions and Maxwell 3D (T/EFIE, K/MFIE) with Raviart–Thomas basis functions.
  • ExaFMMt extension (CFMMExaFMMt): operator-parameter mapping to LaplaceFMMOptions, ModifiedHelmholtzFMMOptions, and HelmholtzFMMOptions; Helmholtz operators with zero wavenumber automatically reduce to the Laplace path.

For a complete list of tested operator–basis combinations see the Supported Operators page.

What could be added

Contributions are welcome in any of the following areas:

Additional FMM backends

The FMMFunctor interface is designed to be extended. Adding a new FMM library requires implementing:

  • CorrectionFactorMatrixMethod.setup(spoints, tpoints, options) — set up the FMM from source and target point matrices.
  • CorrectionFactorMatrixMethod.fmmresult(fmm, x) — evaluate the FMM map for a given input vector.

Potential targets include:

  • A native Julia FMM: A pure-Julia implementation would remove the binary dependency on ExaFMMt and simplify installation, particularly on HPC systems.
  • FMMLIB2D / FMMLIB3D: The Fortran-based FMM libraries wrapped for Julia could serve as an alternative backend.
  • GPU-accelerated FMMs: An extension that drives a GPU FMM (e.g. via CUDA) would be a natural fit for the GPU near-field assembler already used for BEAST.

Two-dimensional operator support

The current collocation and operator implementations target 3D surfaces only. Extending to 2D would require:

  • A 2D sources / potentials implementation for 1D boundary curves.
  • Operators for the 2D Helmholtz and Laplace kernels ($G(\boldsymbol{x},\boldsymbol{y}) = \tfrac{i}{4} H_0^{(1)}(k|\boldsymbol{x}-\boldsymbol{y}|)$).
  • A 2D-capable FMM backend (e.g. FMMLIB2D).

Additional BEM library integrations

The CFMMBEAST extension can serve as a blueprint for integrating other Julia BEM libraries. Any library that can provide quadrature data and block assemblers in the same style can be added as a new extension.

Further operator–basis combinations

Some operator–basis pairings (e.g. Helmholtz operators with Raviart–Thomas basis functions, or Maxwell operators with Lagrange basis functions for mixed formulations) are architecturally supported but not yet tested. Contributions that add and verify such combinations are welcome.

Regenerating documentation plots

The interactive plots on the Examples page are pre-rendered HTML files stored in docs/src/assets/examples/ and committed to the repository. The CI build only serves these static files; it does not re-run the examples.

After changing any file in examples/, regenerate the plots from the repository root:

julia --startup-file=no docs/render_examples.jl

Then commit the updated HTML files alongside your example changes. Running the test suite locally will emit a @warn if the HTML files are older than the example scripts, so you will not accidentally forget this step.

Development workflow

Development targets the dev branch. Open a pull request from dev to main for stable releases and merge with a merge commit.

Run the package checks locally with:

using Pkg
Pkg.test()

Build the documentation with:

using Pkg
Pkg.develop(PackageSpec(path=pwd()))
Pkg.instantiate()
include("docs/make.jl")

Code must pass the formatter, Aqua, JET, and ExplicitImports checks included in the test suite. The formatter is configured with the blue style at a column margin of 92.