{
  "abstract": "The dynamic mode decomposition (DMD), along with its variant for nonlinear systems, the extended DMD (EDMD), are powerful tools for the extraction of meaningful spatio-temporal characteristics of (non)linear dynamical systems from measurement data. Despite some efforts to handle the identification task when dealing with real-world data, the decomposition based methods face a critical challenge: real-world data has two inherent sources of uncertainty, the process and measurement noise. Subspace identification methods, are robust tools able to provide accurate state-space models for multi-variable linear systems directly from input-output data. Combining these two methods, we introduce the p-q quasi-norm Subspace Extended Dynamic Mode Decomposition (pqSEDMD). An algorithm that uses our previous improvements to the EDMD by the use of a p-q-quasi-norm reduction on an orthogonal polynomial basis, the pqEDMD algorithm, along with subspace identification methods. The result is a robust approximation of nonlinear systems in a linear function space, combining the strengths of the two methodologies. Throughout the paper we will use the Duffing oscillator as a benchmark problem to show the effectiveness of the algorithm and illustrate many important aspects related to the development.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.11292",
  "author_total": 2,
  "authors": [
    {
      "name": "Camilo Garcia-Tenorio"
    },
    {
      "name": "Alan Vande Wouwer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1y7h63z6fnagy.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.11292",
  "fulltext_url": null,
  "id": "1y7h63z6fnagy",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.11292",
  "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",
      "math.OC"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7212321037",
  "pdf_url": "https://arxiv.org/pdf/2609.11292",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1y7h63z6fnagy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:30Z",
      "native_id": "2609.11292",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.11292"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "pqSEDMD: Subspace Methods for Extended Dynamic Mode Decomposition Identification",
  "title_text": "pqSEDMD: Subspace Methods for Extended Dynamic Mode Decomposition Identification",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:30Z",
  "url": "https://research.draconitic.com/paper/1y7h63z6fnagy",
  "venue": "arXiv",
  "versions": []
}