{
  "abstract": "Particle-resolved aerosol models preserve the joint size-composition structure that governs cloud activation, optical properties, and freezing, but their computational cost limits their use in large-scale atmospheric models. We extend AeroMELD from a compact representation of aerosol populations to a prognostic model of coagulation. The resulting AeroMELD-Coag advances nine learned coordinates for population shape and one for total particle number through a scale-covariant neural ordinary differential equation that incorporates the known concentration dependence of coagulation. Across 2,000 held-out trajectories spanning 48 h, median symmetric errors are 3.5% for latent shape and 0.52% for total number. This ten-coordinate state also retains the evolving size-resolved composition and associated cloud-condensation-nuclei (CCN) activation, optical properties, and frozen fraction. Compared with the evaluated 20-bin sectional model with 320 prognostic coordinates, AeroMELD-Coag lowers pooled mean CCN activation error from 1.98% to 1.38% and frozen-fraction error from 1.72% to 0.56%. In a matched integration benchmark, GPU integration time per trajectory, amortized over a batch, is more than three orders of magnitude lower than for both single-core CPU references. These coagulation results provide a proof of concept for advancing aerosol microphysics efficiently in a compact learned state while retaining information about aerosol mixing state. They establish a foundation for future large-scale atmospheric models to represent aerosol microphysics with greater mixing-state detail within practical computational budgets.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.31271",
  "author_total": 8,
  "authors": [
    {
      "name": "Wenhan Tang"
    },
    {
      "name": "Ruqi Yang"
    },
    {
      "name": "Jeffrey H. Curtis"
    },
    {
      "name": "Ehsan Saleh"
    },
    {
      "name": "Lekha Patel"
    },
    {
      "name": "Peter A. Bosler"
    },
    {
      "name": "Nicole Riemer"
    },
    {
      "name": "Matthew West"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3cnt6csp2zjf1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.31271",
  "fulltext_url": null,
  "id": "3cnt6csp2zjf1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.31271",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "openalex",
      "2306|Atmospheric aerosols and clouds"
    ]
  ],
  "openalex": "W7214615409",
  "pdf_url": "https://arxiv.org/pdf/2609.31271",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3cnt6csp2zjf1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:52Z",
      "native_id": "2609.31271",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.31271"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "Learning Aerosol Coagulation Dynamics in Latent Space with a Scale-Covariant Neural ODE",
  "title_text": "Learning Aerosol Coagulation Dynamics in Latent Space with a Scale-Covariant Neural ODE",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:52Z",
  "url": "https://research.draconitic.com/paper/3cnt6csp2zjf1",
  "venue": "arXiv",
  "versions": []
}