{
  "abstract": "This paper presents an efficient strategy for constructing Reduced-Order Model (ROM) bases using Taylor polynomial expansions and Fréchet derivatives with respect to model parameters. The proposed approach enables the construction of ROM bases with minimal additional computational cost. By exploiting Fréchet derivatives – solution to the same problem with distinct right-hand sides – the method introduces a streamlined multiple-right-hand-side (RHS) strategy for ROM bases construction. This approach not only reduces overall computational expenses but also improves accuracy during model parameter updates. Numerical experiments on a two-dimensional wave problem demonstrate significant efficiency gains and enhanced performance, highlighting the potential of the proposed method to advance computational cost-effectiveness, particularly in seismic inversion applications.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Julien Besset"
    },
    {
      "name": "Hélène Barucq"
    },
    {
      "name": "Rabia Djellouli"
    },
    {
      "name": "Stefano Frambati"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7tqajn3xskhjn.bib",
  "cited_by": 0,
  "doi": "10.1016/j.cam.2026.118249",
  "fulltext_url": null,
  "id": "7tqajn3xskhjn",
  "issns": [
    "0377-0427"
  ],
  "landing_url": "https://inria.hal.science/hal-05828933v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.math"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W4410101228",
  "pdf_url": "https://inria.hal.science/hal-05828933/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/7tqajn3xskhjn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:00Z",
      "native_id": "hal-05828933",
      "source": "hal",
      "url": "https://inria.hal.science/hal-05828933v1"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3109|Model Reduction and Neural Networks",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "MOR-TL: A Novel Model Order Reduction Method for Parametrized Problems with Application to Seismic Wave Propagation",
  "title_text": "MOR-TL: A Novel Model Order Reduction Method for Parametrized Problems with Application to Seismic Wave Propagation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:00Z",
  "url": "https://research.draconitic.com/paper/7tqajn3xskhjn",
  "venue": "Journal of Computational and Applied Mathematics",
  "versions": []
}