{
  "abstract": "Fast and accurate simulation of hemodynamic behavior within vascular networks is essential for numerous clinical applications. However, obtaining high-quality and computationally efficient flow measurements across complex vascular networks remains challenging. To address this, we first decompose the vascular network into a set of bifurcation units and then develop an operator network capable of mapping unit-specific parameters to the local solution fields of each bifurcation unit. By lumping the Windkessel-model outlet parameters and incorporating inlet boundary conditions from the solution of parent units, the flow and pressure fields can be rapidly approximated. Subsequently, operator-network-driven Schwarz waveform relaxation is applied across bifurcation units to correct discontinuities and improve numerical accuracy. On 7-segment and 55-segment arterial tree models, the proposed method achieves $13\\times$ to $17\\times$ wall-clock speedups over conventional 1D numerical simulation, with relative $L^2$ errors of 1% in both pressure and velocity. The resulting pulse wave velocity biomarkers agree with the conventional reference to within 1--2%, and the same trained operator generalizes to different tree-like 1D vascular network topologies.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.02238",
  "author_total": 5,
  "authors": [
    {
      "name": "Yuzhou Zhao"
    },
    {
      "name": "Han Zhang"
    },
    {
      "name": "J. Matias Di Martino"
    },
    {
      "name": "Jean-Michel Morel"
    },
    {
      "name": "Guillermo Sapiro"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ekq0cxcre76jn.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.02238",
  "fulltext_url": null,
  "id": "ekq0cxcre76jn",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.02238",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CE"
    ],
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7220455634",
  "pdf_url": "https://arxiv.org/pdf/2610.02238",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ekq0cxcre76jn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:22Z",
      "native_id": "2610.02238",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.02238"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    }
  ],
  "title": "A Bifurcation-Based Domain Decomposition Method with Neural Operators for Blood Flow Simulation",
  "title_text": "A Bifurcation-Based Domain Decomposition Method with Neural Operators for Blood Flow Simulation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:22Z",
  "url": "https://research.draconitic.com/paper/ekq0cxcre76jn",
  "venue": "arXiv",
  "versions": []
}