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Learning Spectral-Like Mesh-Free Discretisations

Paper recorded by Signals 4 on 2026-09-02 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-02 on arXiv · recorded by Signals 4 on 2026-09-03

Category: cs.LG · 机器学习 · first seen 2026-09-03

Abstract

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 containing more nodes than there are consistency constraints, the resulting linear sy

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#148 most recent of 215 cs.LG papers we have recorded · ↑ newer: UE5M3 FP4 Block Scaling for Stable Language Model Pretraining · ↓ older: Cliff: Learning Process Rewards from the First Mistake
Cite this page: Learning Spectral-Like Mesh-Free Discretisations: the #148 most recent of 215 cs.LG papers we have recorded (as of 2026-09-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/learning-spectral-like-mesh-free-discretisations.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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