{
  "abstract": "Modeling the Martian nightside thermosphere remains challenging due to sparse in situ sampling and strong coupling among transport, magnetic, and seasonal processes. Purely data-driven models can produce non-physical artifacts, such as density inversions, in poorly sampled altitude regimes. We present a multi-task physics-informed neural network that simultaneously predicts the base-10 logarithmic densities of four neutral species (O, CO$_2$, N$_2$, and Ar) using more than a decade of MAVEN/NGIMS observations (MY 32-38, 2014-2025). A shared backbone learns a common representation of the nightside thermospheric state and branches into species-specific output heads. A weak monotonicity prior is incorporated via automatic differentiation by penalizing positive vertical gradients in logarithmic density. Experiments using an orbit-disjoint train/validation/test split show that physics-informed regularization substantially reduces non-physical inversions while preserving predictive skill and slightly improving it in the best-performing configuration, as measured by RMSE, MAE, and $R^2$. The resulting model provides a computationally efficient surrogate for nightside thermospheric reconstruction with improved vertical consistency.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.10077",
  "author_total": 1,
  "authors": [
    {
      "name": "Sergey Nikiforov"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6dhjgn08asnzz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.10077",
  "fulltext_url": null,
  "id": "6dhjgn08asnzz",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.10077",
  "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",
      "astro-ph.EP"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "openalex",
      "3103|Planetary Science and Exploration"
    ]
  ],
  "openalex": "W7212150518",
  "pdf_url": "https://arxiv.org/pdf/2609.10077",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6dhjgn08asnzz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:12Z",
      "native_id": "2609.10077",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.10077"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.EP",
      "subfield": "planetary"
    }
  ],
  "title": "Physics-Informed Multi-Task Surrogate Model for the Martian Nightside Thermosphere",
  "title_text": "Physics-Informed Multi-Task Surrogate Model for the Martian Nightside Thermosphere",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:12Z",
  "url": "https://research.draconitic.com/paper/6dhjgn08asnzz",
  "venue": "arXiv",
  "versions": []
}