{
  "abstract": "We present a computational framework for robust topology optimization that integrates polygonal finite-element discretizations, spatially correlated non-Gaussian material modeling, and non-intrusive polynomial-chaos surrogates. Spatial uncertainty in Young's modulus is represented as a homogeneous non-Gaussian random field obtained via a memoryless transformation of a truncated Karhunen-Loève expansion, ensuring physical admissibility through positivity of stiffness while preserving the prescribed autocovariance. Polygonal finite elements provide a stable discretization for density-based optimization on unstructured meshes and mitigate checkerboard artefacts and mesh bias, while the sparse polynomial-chaos expansion enables efficient estimation of low-order statistical moments required by the robust objective at a fraction of the cost of intrusive or Monte Carlo approaches. Numerical studies on a cantilever and a curved beam show that introducing non-Gaussian material variability leads to systematic load-path redistribution and a reallocation of 6-12% of the structural volume, together with a reduction in compliance scatter. The non-intrusive surrogate reproduces intrusive reference results within 3% using an order of magnitude fewer full finite-element analyses. These results demonstrate that the proposed framework offers a physically consistent and computationally efficient route to topology-optimized designs that remain reliable under realistic material uncertainty.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: J Braz. Soc. Mech. Sci. Eng. 48, 665 (2026)",
  "arxiv": "2609.05466",
  "author_total": 4,
  "authors": [
    {
      "name": "Nilton Cuellar"
    },
    {
      "name": "Anderson Pereira"
    },
    {
      "name": "Ivan F. M. Menezes"
    },
    {
      "name": "Americo Cunha Jr"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dzx6ypqpvjs8v.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.05466",
  "fulltext_url": null,
  "id": "dzx6ypqpvjs8v",
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  "landing_url": "https://arxiv.org/abs/2609.05466",
  "language": null,
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  "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.CE"
    ],
    [
      "arxiv",
      "cs.NA"
    ],
    [
      "arxiv",
      "math.NA"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "arxiv",
      "stat.CO"
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    [
      "arxiv",
      "stat.ME"
    ]
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  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.05466",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dzx6ypqpvjs8v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:58Z",
      "native_id": "2609.05466",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.05466"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.NA",
      "subfield": "numerical-analysis"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.CO",
      "subfield": "statistics"
    }
  ],
  "title": "Robust topology optimization with non-Gaussian material fields using polygonal finite elements",
  "title_text": "Robust topology optimization with non-Gaussian material fields using polygonal finite elements",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:07Z",
  "url": "https://research.draconitic.com/paper/dzx6ypqpvjs8v",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1007/s40430-026-06564-7",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
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  ]
}