{"slug": "enriched-holomorphic-neural-networks-for-2d-and-3d-laplace-problems", "title": "Enriched Holomorphic Neural Networks for 2D and 3D Laplace Problems", "summary": "Researchers combined holomorphic neural networks with geometry-derived singular functions to solve 2D and 3D Laplace boundary-value problems, reporting that the enriched model was the most accurate method tested on every problem. According to the arXiv:2610.12541v1 abstract, the harmonic-by-construction representation needs no interior residual and trains singular exponents, giving margins of several orders of magnitude where the enrichment captures the singularity exactly and a factor of about 1.7-2.1 over the next-best method on a CdZnTe radiation detector with many singular sites in both two and three dimensions.", "body_md": "arXiv:2610.12541v1 Announce Type: new \nAbstract: Boundary-value problems for the Laplace equation develop singular behaviour near corners, edges and junctions that smooth approximators resolve poorly. We combine holomorphic neural networks with singular functions derived from the local geometry and boundary conditions. The representation is harmonic by construction, so no interior residual is needed: the network fits the smooth remainder from boundary data alone, and the enrichment terms capture the singular behaviour. Singular exponents are trainable, so the construction also applies when the local expansion is not known in advance. We extend the approach to three dimensions using independent networks evaluated along complex null directions. The enriched model is the most accurate tested method on every problem. Its margin is several orders of magnitude where the enrichment captures the singularity exactly, and a factor of about 1.7-2.1 over the next-best method on a CdZnTe radiation detector with many singular sites, in both two and three dimensions.", "url": "https://wpnews.pro/news/enriched-holomorphic-neural-networks-for-2d-and-3d-laplace-problems", "canonical_source": "https://www.machinebrief.com/news/enriched-holomorphic-neural-networks-for-2d-and-3d-laplace-p-58qn", "published_at": "2026-10-12 04:00:00+00:00", "updated_at": "2026-10-12 04:06:30.660566+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "ai-research"], "entities": ["arXiv", "CdZnTe"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/enriched-holomorphic-neural-networks-for-2d-and-3d-laplace-problems", "markdown": "https://wpnews.pro/news/enriched-holomorphic-neural-networks-for-2d-and-3d-laplace-problems.md", "text": "https://wpnews.pro/news/enriched-holomorphic-neural-networks-for-2d-and-3d-laplace-problems.txt", "jsonld": "https://wpnews.pro/news/enriched-holomorphic-neural-networks-for-2d-and-3d-laplace-problems.jsonld"}}