{
  "abstract": "We develop a divergence-kernel method for jointly optimizing neural SDE drift and diffusion fields and the initial Gaussian distribution, using losses that depend directly on the terminal density. In two- and eight-dimensional Gaussian-mixture experiments, we learn time-independent fields and obtain far fewer off-mode samples than score-based generators with similar parameter counts. The drift attracts samples toward the modes, while the diffusion helps disperse excess probability between them.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41243",
  "author_total": 1,
  "authors": [
    {
      "name": "Angxiu Ni"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8krrq3bqrzd4w.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41243",
  "fulltext_url": null,
  "id": "8krrq3bqrzd4w",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41243",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "math.DS"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41243",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8krrq3bqrzd4w.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:20Z",
      "native_id": "2610.41243",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41243"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.DS",
      "subfield": "dynamical-systems"
    }
  ],
  "title": "Generative SDEs with Trainable Diffusion and Density-Based Losses",
  "title_text": "Generative SDEs with Trainable Diffusion and Density-Based Losses",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:52Z",
  "url": "https://research.draconitic.com/paper/8krrq3bqrzd4w",
  "venue": "arXiv",
  "versions": []
}