Multifidelity Modeling

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Overview

Multifidelity modeling uses data or simulations at several levels of cost and accuracy—for example, coarse-mesh computational fluid dynamics, reduced-order models, and laboratory measurements—to learn predictions or infer parameters with fewer expensive evaluations. Common approaches include Gaussian-process co-kriging and discrepancy models, neural transfer or residual correction, and adaptive sampling for surrogate modeling, Bayesian optimization, and simulation-based inference. Applications span partial differential equations, materials and chemistry, and engineering design, where high-fidelity data are often limited. The main challenge is deciding when low-fidelity information helps: fidelity gaps can be biased or regime-dependent, uncertainty must remain calibrated, and adding fidelity levels may not justify their cost.

Latest papers

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36 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

  1. A Unified Hierarchical Multi-Task Multi-Fidelity Framework for Data-Efficient Surrogate Modeling in Manufacturing

    Mar 10, 2026Manan Mehta, Zhiqiao Dong, Yuhang Yang +1Surrogate ModelingMultifidelity Modeling

    Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States · Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 41809, United States

  2. A Consistency-Centric Approach to Set-Based Optimization with Multiple Models of Unranked Fidelity

    Feb 28, 2026Danielle F. Morey, Giulia Pedrielli, Cherry Y. Wakayama +1Multi-Fidelity OptimizationMultifidelity Modeling

    Industrial and Systems Engineering, University of Washington, Seattle WA, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe AZ, USA · Intelligence, Surveillance, and Reconnaissance Department, Naval Information Warfare Center Pacific, San Diego CA, USA

  3. MAST: A Multi-fidelity Augmented Surrogate model via Spatial Trust-weighting

    Feb 24, 2026Ahmed Mohamed Eisa Nasr, Ali Elham, Haris Moazam SheikhSurrogate ModelingMultifidelity Modeling

    Department of Aeronautics and Astronautics, University of Southampton, SO17 1BJ, Southampton, United Kingdom

  4. When Does Cheap Physics Help? Gated Multi-Fidelity Physics-Informed Networks with Provable Sample Savings and Conformal Guarantees

    Feb 1, 2026laf Yunus Laitinen Imanov, Rana Irem Turhan, Isgandar JafarliMultifidelity ModelingPhysics-Informed ML

    Department of Applied Mathematics and Computer Science (DTU Compute), Technical University of Denmark, Kongens Lyngby, Denmark

  5. FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models

    Jan 29, 2026Rosen Ting-Ying Yu, Nicholas Sung, Faez AhmedMultifidelity Modeling

    Center for Computational Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA · Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

  6. Bifidelity Parameter Estimation Using Conditional Diffusion Models

    Apr 2, 2025Caroline Tatsuoka, Minglei Yang, Dongbin Xiu +1Simulation-Based InferenceSurrogate Modeling

    Department of Mathematics, The Ohio State University, Columbus, OH 43210, USA · Fusion Energy Science Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA · Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA