Author

S. Lind

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Preprint Sep 2026

Learning Spectrally Optimised Mesh-Free Discretisations

Spectrally optimised Neural Discretisations (SpeND), a framework that learns the map from local stencil geometry to discretisation weights on unstructured point clouds, and has been shown to reduce the wall-clock time between $3-20\times$ to achieve equivalent error as the baselines.

Lucas Gerken Starepravo, Henry M. Broadley, S. Lind et al. · 0 citations

#machine learning Preprint Sep 2026

Learning Spectral-Like Mesh-Free Discretisations

Meshfree methods such as smoothed particle hydrodynamics (SPH) with kernel corrections, radial basis function-generated finite differences (RBF-FD), and the local anisotropic basis function method (LABFM) construct discrete differential operators by imposing polynomial consistency on a local stencil. For stencils conta...

Lucas Gerken Starepravo, Henry M. Broadley, S. Lind et al. · 0 citations