{
  "abstract": "A wide range of methods have been proposed, including physics-informed neural networks, which are powerful but do not guarantee identifiability of the dynamics, symbolic regression, which requires a set of precomputed operations, and causal discovery, which is more principled but usually relies on strong assumptions that physical systems may violate. In this work, we develop a theory-grounded method and prove that under a set of permissive assumptions, the structural drivers and drift of stochastic delayed differential equations are identifiable. Our method outperforms others on a benchmark for driver identifiability, and on a second benchmark to evaluate physical consistency of the learned dynamics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.37944",
  "author_total": 3,
  "authors": [
    {
      "name": "Julien Boussard"
    },
    {
      "name": "Antoine Debouchage"
    },
    {
      "name": "Théo Saulus"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a4gc6ghbw0jwb.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.37944",
  "fulltext_url": null,
  "id": "a4gc6ghbw0jwb",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.37944",
  "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.LG"
    ],
    [
      "arxiv",
      "math.DS"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7214892286",
  "pdf_url": "https://arxiv.org/pdf/2609.37944",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a4gc6ghbw0jwb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:07Z",
      "native_id": "2609.37944",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.37944"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.DS",
      "subfield": "dynamical-systems"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems",
  "title_text": "Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:14Z",
  "url": "https://research.draconitic.com/paper/a4gc6ghbw0jwb",
  "venue": "arXiv",
  "versions": []
}