{
  "abstract": "Wormlike micellar solutions exhibit formulation-dependent nonlinear startup responses, including stress overshoot, undershoot, and strain hardening, making prediction across compositions and shear rates challenging. We developed a multi-fidelity machine-learning framework to predict stress-growth curves in cetylpyridinium chloride (CPyCl)-based wormlike micelles from sparse experimental measurements. The framework combines a low-fidelity analytical model describing the overall startup response with high-fidelity experimental data capturing formulation-specific nonlinear features. This approach addresses the limited and uneven coverage of salt chemistry, concentration, and shear rate without relying solely on data-intensive neural-network training. The trained model predicts complete stress-growth responses at untested shear rates and enables extraction of strain-hardening characteristics from interpolated curves. By augmenting sparse rheological measurements, the framework provides a basis for exploring relationships among formulation variables, linear viscoelastic properties, and nonlinear startup behavior, while supporting more efficient characterization of wormlike micellar solutions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.04115",
  "author_total": 6,
  "authors": [
    {
      "name": "Saghar Zolfaghari"
    },
    {
      "name": "Nunzia Lauriello"
    },
    {
      "name": "Ilaria Cusano"
    },
    {
      "name": "Marcello Ferranti"
    },
    {
      "name": "Rossana Pasquino"
    },
    {
      "name": "Safa Jamali"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/90b67nxex7g0c.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.04115",
  "fulltext_url": null,
  "id": "90b67nxex7g0c",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.04115",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.soft"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.04115",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/90b67nxex7g0c.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:25Z",
      "native_id": "2611.04115",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.04115"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.soft",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Multi-Fidelity Machine Learning for Predicting Startup Stress Growth and Strain Hardening in Wormlike Micellar Solutions",
  "title_text": "Multi-Fidelity Machine Learning for Predicting Startup Stress Growth and Strain Hardening in Wormlike Micellar Solutions",
  "type": "preprint",
  "updated_at": "2026-11-06T18:21:01Z",
  "url": "https://research.draconitic.com/paper/90b67nxex7g0c",
  "venue": "arXiv",
  "versions": []
}