{
  "abstract": "We introduce a gravity-informed neural-network (GravINNs) framework for learning post-Newtonian binary orbital dynamics. The method incorporates the equations of motion directly into the training phase and is developed at three complementary levels. First, we construct single-orbit surrogates for conservative post-Newtonian dynamics and assess their accuracy across different orbital configurations. We then extend the approach to a parametric model spanning a four-dimensional parameter space defined by the initial separation, radial and tangential momenta, and symmetric mass ratio, allowing a single trained network to represent entire families of orbits and replace repeated numerical integrations. Finally, we consider dissipative dynamics by including the leading radiation-reaction contribution at $2.5$PN order. In this regime, the network reproduces the orbital evolution and the associated secular energy loss, starting from a conservative 2PN warm start and without using dissipative reference trajectories in the training. These results show that physics-informed neural-networks (PINNs) can provide accurate and flexible representations of both conservative and dissipative post-Newtonian dynamics. The complete implementation and the models used in this work are publicly available through the \\texttt{GravINNs} repository.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.25222",
  "author_total": 2,
  "authors": [
    {
      "name": "Gabriele Barbagallo"
    },
    {
      "name": "Javier Matulich"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7r0bswf6dcxf7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.25222",
  "fulltext_url": null,
  "id": "7r0bswf6dcxf7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.25222",
  "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",
      "gr-qc"
    ],
    [
      "openalex",
      "3103|Pulsars and Gravitational Waves Research"
    ]
  ],
  "openalex": "W7214096337",
  "pdf_url": "https://arxiv.org/pdf/2609.25222",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7r0bswf6dcxf7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:22Z",
      "native_id": "2609.25222",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.25222"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:gr-qc",
      "subfield": "gravitation"
    }
  ],
  "title": "Gravity-Informed Neural Networks for Post-Newtonian Binary Dynamics",
  "title_text": "Gravity-Informed Neural Networks for Post-Newtonian Binary Dynamics",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:44Z",
  "url": "https://research.draconitic.com/paper/7r0bswf6dcxf7",
  "venue": "arXiv",
  "versions": []
}