{
  "abstract": "Bubbly flows involve strongly coupled bubble motion, deformation, and hydrodynamic interactions, making high-fidelity simulation computationally expensive. We propose a neural surrogate that jointly predicts bubble trajectories and interface deformation in a compact, shared representation, replacing costly mesh-resolved simulation with fast generative inference. Because two-phase flow dynamics is nonlinear and chaotic, we treat forecasting as a conditional generative problem rather than deterministic regression, predicting entire future windows in a single pass. We train and evaluate the surrogate on BubbleSH, a Front-Tracking dataset of rising deformable bubble swarms, assessing performance using trajectory, shape, and distributional metrics over kinematic, morphological, and interaction-based properties. The surrogate reproduces key flow statistics while running several orders of magnitude faster than the underlying DNS solver.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.16327",
  "author_total": 5,
  "authors": [
    {
      "name": "Kiet Bennema ten Brinke"
    },
    {
      "name": "Rachna Ramesh"
    },
    {
      "name": "Douwe Orij"
    },
    {
      "name": "Ivo Roghair"
    },
    {
      "name": "Vlado Menkovski"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ckwnqwqhmmj25.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.16327",
  "fulltext_url": null,
  "id": "ckwnqwqhmmj25",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.16327",
  "language": null,
  "licence": "CC-BY-SA-4.0",
  "licence_label": "CC BY-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.flu-dyn"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7221799681",
  "pdf_url": "https://arxiv.org/pdf/2610.16327",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ckwnqwqhmmj25.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2610.16327",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.16327"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics",
  "title_text": "Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/ckwnqwqhmmj25",
  "venue": "arXiv",
  "versions": []
}