{
  "annotation_id": "a0706d9b-2901-4778-af88-581a42250ad2",
  "date_created": "2026-02-17T05:53:19.951000Z",
  "date_modified": "2026-02-17T05:53:19.951000Z",
  "file_hash": "7629cf31ed9cdab8862367f42683660bdf58c9e6cd0b3a42cbc16288b646a47d",
  "private": false,
  "record": {
    "abstract": "We propose two globally continuous neural-based variants of the Neural Approximated Virtual Element Method (NAVEM), termed B-NAVEM and P-NAVEM. Both approaches construct local basis functions using pre-trained fully connected neural networks while ensuring exact continuity across adjacent mesh elements. B-NAVEM leverages a Physics-Informed Neural Network to approximately solve the local Laplace problem that defines the virtual element basis functions, whereas P-NAVEM directly enforces polynomial reproducibility via a tailored loss function, without requiring harmonicity within the element interior. Numerical experiments assess the methods in terms of computational cost, memory usage, and accuracy during both training and testing phases.",
    "arxiv_id": "2601.09595",
    "authors": [
      "Stefano Berrone",
      "Moreno Pintore",
      "Gioana Teora"
    ],
    "categories": [
      "math.NA",
      "cs.NA"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "title": "Two continuous extensions of the Neural Approximated Virtual Element Method",
    "url": "https://arxiv.org/abs/2601.09595",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "810548d3-3132-465c-b845-c3211d0f22bc",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}