{
  "abstract": "Predictions with dynamical systems can be computationally expensive---even with reduced models---because reaching future prediction times can require many sequential time integration steps. We propose Flow-Map First-Order Trajectory Matching (FM-FTM), a nonintrusive model reduction approach that accelerates ensemble predictions of stochastic dynamical systems by learning flow maps that replace multiple time-integration steps. Our method uses first-order trajectory matching (FTM) to learn a deterministic representation of the stochastic probability-current dynamics and then reduces its evolution over time into a flow map whose evaluation directly yields predictions over long time horizons. Numerical experiments on evolution problems with stochastic forcing demonstrate that FM-FTM accelerates ensemble predictions by up to two orders of magnitude while accurately predicting time marginals and ensemble statistics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.35504",
  "author_total": 2,
  "authors": [
    {
      "name": "Shreya Jha"
    },
    {
      "name": "Benjamin Peherstorfer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0t6vwsjymfjvd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.35504",
  "fulltext_url": null,
  "id": "0t6vwsjymfjvd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.35504",
  "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",
      "cs.NA"
    ],
    [
      "arxiv",
      "math.NA"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7223962918",
  "pdf_url": "https://arxiv.org/pdf/2610.35504",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0t6vwsjymfjvd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:40Z",
      "native_id": "2610.35504",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.35504"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.NA",
      "subfield": "numerical-analysis"
    }
  ],
  "title": "Model reduction in time: Learning flow maps for reducing stochastic dynamical systems in time",
  "title_text": "Model reduction in time: Learning flow maps for reducing stochastic dynamical systems in time",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:40Z",
  "url": "https://research.draconitic.com/paper/0t6vwsjymfjvd",
  "venue": "arXiv",
  "versions": []
}