{
  "abstract": "Risk-averse design optimization with conditional value-at-risk (CVaR) constraints requires accurate tail estimates, but repeated high-fidelity (HF) evaluations are costly. We propose a multifidelity (MF) tail-region correction method that uses dimensionally decomposed generalized polynomial chaos expansion (DD-GPCE) as a global surrogate and directs a limited HF budget to responses that most strongly influence CVaR. Covariance of the estimated DD-GPCE coefficients quantifies finite-sample prediction uncertainty and defines a confidence-interval-based tail region. Within this region, a two-stage strategy explores locations with high prediction uncertainty and then exploits locations with large correction-induced shifts. A Tikhonov-regularized residual expansion using the same polynomial basis yields a unified tail-corrected surrogate. For a ten-bar truss, the method produced a design close to the crude-MCS reference with approximately 72 to 274 times fewer HF evaluations than the considered MF importance sampling configurations. For a suction valve, it reduced mass by 1.05% and bending and bulging CVaR by 17.68% and 21.53%, respectively, while satisfying the valve-opening requirement. The results demonstrate accurate, HF-sample-efficient CVaR-constrained design through localized MF correction.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.29222",
  "author_total": 2,
  "authors": [
    {
      "name": "Gyeolhee Lee"
    },
    {
      "name": "Dongjin Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5596e5gqvbfhs.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.29222",
  "fulltext_url": null,
  "id": "5596e5gqvbfhs",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.29222",
  "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",
      "math.OC"
    ],
    [
      "openalex",
      "1804|Probabilistic and Robust Engineering Design"
    ]
  ],
  "openalex": "W7204915068",
  "pdf_url": "https://arxiv.org/pdf/2608.29222",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5596e5gqvbfhs.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:48Z",
      "native_id": "2608.29222",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.29222"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Risk-averse design optimization with CVaR constraints via multifidelity tail-region correction",
  "title_text": "Risk-averse design optimization with CVaR constraints via multifidelity tail-region correction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:48Z",
  "url": "https://research.draconitic.com/paper/5596e5gqvbfhs",
  "venue": "arXiv",
  "versions": []
}