{
  "abstract": "Complex transient energy systems require uncertainty-quantified predictions, but Bayesian inverse uncertainty quantification (IUQ) is often impractical because posterior sampling with high-fidelity system codes requires many expensive full-transient simulations. This study develops a surrogate-assisted Bayesian IUQ framework that makes full-time-series calibration practical for the NACIE heavy-liquid-metal natural-circulation loop. The key innovation is to transform a black-box transient simulator into a differentiable probabilistic emulator by combining principal component analysis with Gaussian process regression (PCA--GPR). The surrogate reconstructs complete multichannel temperature and mass-flow histories from a compact latent representation, preserving dominant temporal and cross-channel correlations while enabling automatic differentiation of the log posterior. This allows both gradient-free ensemble slice sampling and gradient-informed sampling with the No-U-Turn Sampler and Microcanonical Langevin Monte Carlo. Five principal components capture more than 99\\% of the output variance, and the surrogate achieves normalized root-mean-square errors below 2\\%. Compared with direct Flownex SE evaluations, the surrogate reduces likelihood-evaluation cost by approximately 3000-fold while preserving posterior statistics and correlations. Posterior predictive analyses show reduced bias, narrower intervals, and improved agreement with measurements and independent validation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.20978",
  "author_total": 3,
  "authors": [
    {
      "name": "Zikang Yaoa"
    },
    {
      "name": "Eugene Yeea"
    },
    {
      "name": "Fue-Sang Lien"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/73wj08x9s7m46.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.20978",
  "fulltext_url": null,
  "id": "73wj08x9s7m46",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.20978",
  "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",
      "physics.comp-ph"
    ],
    [
      "arxiv",
      "physics.flu-dyn"
    ],
    [
      "openalex",
      "2202|Nuclear Engineering Thermal-Hydraulics"
    ]
  ],
  "openalex": "W7222642042",
  "pdf_url": "https://arxiv.org/pdf/2610.20978",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/73wj08x9s7m46.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:50Z",
      "native_id": "2610.20978",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.20978"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.flu-dyn",
      "subfield": "fluid-dynamics"
    }
  ],
  "title": "A differentiable PCA-GPR surrogate framework for full-transient Bayesian inverse uncertainty quantification of black-box nuclear thermal-hydraulic systems",
  "title_text": "A differentiable PCA-GPR surrogate framework for full-transient Bayesian inverse uncertainty quantification of black-box nuclear thermal-hydraulic systems",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:50Z",
  "url": "https://research.draconitic.com/paper/73wj08x9s7m46",
  "venue": "arXiv",
  "versions": []
}