{
  "abstract": "We develop a multifidelity method for fusing low-resolution simulations with computationally expensive high-resolution simulations, which are run infrequently and are therefore prone to bias. We formulate this fusion as a constrained optimization under missing-not-at-random (MNAR) selection bias. This formulation searches for the exponentially tilted high-resolution distribution that minimizes KL divergence from the biased baseline, subject to moment constraints derived from low-resolution simulations. This optimization requires first estimating the biased baseline conditional density $f$ as a nuisance parameter. We estimate $f$ using a score-based diffusion model. To eliminate the generative model's regularization bias that harms the downstream task, we apply targeted maximum likelihood estimation (TMLE). TMLE debiases $\\hat{f}$ via a targeted exponential tilting, rendering the target parameters insensitive to first-order nuisance estimation errors. To execute this computationally, we adapt generative model steering, a technique originally developed for human-preference alignment. Using Feynman-Kac steering with a reward function based on our formulation, we simultaneously execute the exponential tilts for MNAR and TMLE at inference time, avoiding expensive retraining costs. Code available [here](https://github.com/Jong-Min-Moon/multifidel_emul_by_FK).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.30096",
  "author_total": 1,
  "authors": [
    {
      "name": "Jongmin Mun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3f58vewe94dk7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.30096",
  "fulltext_url": null,
  "id": "3f58vewe94dk7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.30096",
  "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",
      "stat.CO"
    ],
    [
      "openalex",
      "1702|Gaussian Processes and Bayesian Inference"
    ]
  ],
  "openalex": "W7204887196",
  "pdf_url": "https://arxiv.org/pdf/2608.30096",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3f58vewe94dk7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:54Z",
      "native_id": "2608.30096",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.30096"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.CO",
      "subfield": "statistics"
    }
  ],
  "title": "Multifidelity Computer Model Emulation Via Diffusion Model Steering and Targeted Maximum Likelihood",
  "title_text": "Multifidelity Computer Model Emulation Via Diffusion Model Steering and Targeted Maximum Likelihood",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:54Z",
  "url": "https://research.draconitic.com/paper/3f58vewe94dk7",
  "venue": "arXiv",
  "versions": []
}