{
  "abstract": "Sparse equation discovery depends on the candidate functions, the treatment of noisy data, and the sparse regression procedure. These components are commonly configured independently, although their interactions directly affect model recovery. Our data-driven model optimizer ddmo uses PySINDy as its default sparse-regression backend and adds a modular, multiobjective optimization layer around it. Within one configurable search, users can vary parameters of prescribed candidate functions, regression settings, and the test functions and windows used to construct weak-form equations. For each configuration, the package builds the regression problem, estimates model coefficients with the selected backend, and evaluates the resulting model against objectives that include predictive accuracy and sparsity. This workflow makes it possible to explore how these modeling choices interact without writing a separate optimization pipeline for each study. We demonstrate the framework using the nonlinear Goodwin oscillator under increasing levels of measurement noise. The optimized weak formulation improves noise robustness relative to the strong formulation and supports accurate recovery of sparse dynamics. Joint library optimization explores competing nonlinear function families and estimates their parameters. The package provides a unified optimization layer for investigating how library design, weak formulation, and regularization jointly affect sparse equation discovery.",
  "abstract_shown": true,
  "affiliations": [
    "Fritz-Haber-Institut of the Max Planck Society, Faradayweg 4–6, Berlin, Germany, 14195",
    "University of Colorado Boulder"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Juan Manuel Lombardi",
      "orcid": "0009-0002-3961-1426"
    },
    {
      "affs": [
        0
      ],
      "name": "Maryke Kouyate"
    },
    {
      "affs": [
        0
      ],
      "name": "Susanne Fürst"
    },
    {
      "affs": [
        1
      ],
      "name": "Rainey Lyons"
    },
    {
      "affs": [
        0
      ],
      "name": "Christian Kunkel"
    },
    {
      "affs": [
        0
      ],
      "name": "Karsten Reuter"
    },
    {
      "affs": [
        0
      ],
      "name": "Christoph Scheurer"
    },
    {
      "affs": [
        0
      ],
      "name": "Gianmarco Ducci",
      "orcid": "0000-0003-0681-0074"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5tygrc0xh9dm9.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010479/v1",
  "fulltext_url": null,
  "id": "5tygrc0xh9dm9",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010479/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7221781436",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010479/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/5tygrc0xh9dm9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010479",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010479/v1"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3109|Model Reduction and Neural Networks",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "ddmo: A Compatible PySINDy Engine for Feature Optimization in Sparse Dynamical-System Discovery",
  "title_text": "ddmo: A Compatible PySINDy Engine for Feature Optimization in Sparse Dynamical-System Discovery",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/5tygrc0xh9dm9",
  "venue": "ChemRxiv",
  "versions": []
}