Multifidelity Modeling

Ask about this topic

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

CardsList

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

  1. Multi-Fidelity Training Reshapes Uncertainty Decomposition in Atomistic Models

    Oct 26, 2026Matteo Tolloso, Davide Bacciu, Luigi BonatiMultifidelity ModelingUncertainty Calibration

    Department of Computer Science, University of Pisa · Atomistic Simulations, Italian Institute of Technology

  2. Compute-optimal data scaling for neural surrogates via multi-fidelity training

    Oct 12, 2026Paul Setinek, Fabian Paischer, Nils Thuerey +2Neural Surrogate ModelingPDE Surrogate Modeling

    Institute for Machine Learning, JKU Linz · Mistral AI · Technical University of Munich +1

  3. Adaptive hybrid coupling with operator inference, the overlapping Schwarz alternating method and reinforcement learning

    Sep 15, 2026Trishit Mondal, Irina Tezaur, Anthony GruberReinforcement LearningPDE Surrogate Modeling

    Worcester Polytechnic Institute

  4. Toward Reliable Railway-Bogie Response Prediction Using Multifidelity TDNN and Physics-Informed Residual Learning

    Sep 10, 2026Gyeolhee Lee, Moosun Kim, Taewook Kwon +3Surrogate ModelingResidual Learning

    Department of Automotive Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea · Korea Railroad Research Institute, 176 Cheoldobangmulgwan-ro, Uiwang-si, Gyeonggi-do 16105, Republic of Korea · Department of Future Mobility, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea

  5. Selective boundary condition reduction via learned error gating

    Sep 8, 2026Daniel Fernández, Dominik Penk, Dominik RiedelbauchPDE Surrogate ModelingMultifidelity Modeling

    Chair for Dynamics, Control, Machine Learning, and Numerics (Alexander von Humboldt Professorship), Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstraße, 91058 Erlangen, Germany. · Schaeffler Technologies AG & Co. KG, Industriestraße 1–3, Herzogenaurach, Germany.

  6. Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication

    Aug 18, 2026Berkcan Kapusuzoglu, Sankaran MahadevanPhysics-Informed MLAdditive Manufacturing

    Department of Civil and Environmental Engineering Vanderbilt University Nashville, TN 37235, USA

  7. FLARE MCMC: Fidelity-based Layer-Adaptive REcursive proposals for MCMC

    Aug 13, 2026Harini Venkatesan, Christian Shelton, Ming-Feng Ho +2Markov Chain Monte CarloPosterior Sampling

    Department of Computer Science and Engineering, University of California, Riverside · Leinweber Center for Theoretical Physics, University of Michigan · Department of Physics and Astronomy, University of California, Riverside

  8. Out-Of-The-Loop Multi-Fidelity Bayesian Optimization

    Aug 4, 2026Gustavo Sutter, Hao Wang, Luis Ricardez-Sandoval +2Multi-Fidelity OptimizationBayesian Optimization

    Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada · Vector Institute, Toronto, ON, Canada · Department of Chemical Engineering, University of Waterloo, Waterloo, ON, Canada +2

  9. Transfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimization

    Jul 26, 2026Jaewook Lee, Ethan Errington, Christian D. Lorenz +1Multi-Fidelity OptimizationSurrogate Modeling

    Department of Engineering, King’s College London, London, WC2R 2LS, United Kingdom.

  10. Deep Gaussian Processes on Directed Acyclic Graphs

    Jul 10, 2026Federico L. Perlino, Oliver Hamelijnck, Adam M. Johansen +1Surrogate ModelingDeep Gaussian Processes

    Department of Statistics, University of Warwick · 2AI Team, Unilink Software Ltd · Department of Computer Science, University of Warwick

  11. Compressing the Validation Bottleneck: An Agentic Self-Driving Lab for Scientific Discovery

    Jul 5, 2026Kyunghoon Hur, Chihun LeeAI for ScienceMultifidelity Modeling

    AX Research Division, Korea Electronics Technology Institute, Republic of Korea · Material Data Division, Korea Institute of Materials Science, Republic of Korea

  12. Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements

    Jun 14, 2026Filippo Zacchei, Ana Larrañaga, Attilio Frangi +2Sparse Identification of Nonlinear DynamicsNonlinear System Identification

    MOX – Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy · Department of Mechanical Engineering, University of Washington, Seattle, Washington 98195, United States · Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy

  13. Bridged SBI: Correcting Biased Low-Fidelity Posteriors for Cost-Efficient High-Fidelity Inference

    Jun 8, 2026Gahee Kim, Yuki Kadokawa, Sandro M. Alcantara Tacora +5Simulation-Based InferenceRobotics Simulation

    NARA INSTITUTE OF SCIENCE AND TECHNOLOGY, Nara, Japan · PUBLIC WORKS RESEARCH INSTITUTE, Ibaraki, Japan

  14. Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning

    Jun 1, 2026Vivin Vinod, Peter ZaspelQuantum ChemistryMultifidelity Modeling

    School of Mathematics and Natural Sciences, University of Wuppertal, Germany.

  15. Uncertainty-aware Multi-fidelity Closure via Conditional Normalizing Flows

    May 27, 2026Jice Zeng, Shady E. Ahmed, David Barajas-Solano +1Uncertainty QuantificationMultifidelity Modeling

    Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, Richland, 99352, WA, USA

  16. Multi-Fidelity Learning with Shallow Recurrent Decoders for Reactor Physics

    May 22, 2026Stefano Riva, Carolina Introini, J. Nathan Kutz +1Neural Surrogate ModelingRecurrent Neural Networks

    Autodesk Research, London, UK · Department of Energy, Nuclear Engineering Division, Politecnico di Milano, Milan, Italy · Department of Mechanical and Nuclear Engineering and Emirates Nuclear Technology Center, Khalifa University, Abu Dhabi (127788), United Arab Emirates

  17. Multi-Fidelity Flow Matching: Cascaded Refinement of PDE Solutions

    May 15, 2026Sipeng Chen, Junliang Liu, Hewei Tang +1Surrogate ModelingConditional Flow Matching

    Department of Computer Science, Florida State University · Department of Materials Science and Engineering, Florida State University · Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin

  18. Multifidelity Gaussian process regression for solving nonlinear partial differential equations

    May 11, 2026Fatima-Zahrae El-Boukkouri, Josselin Garnier, Olivier RoustantSurrogate ModelingPDE Solving

    Institut de Mathématiques de Toulouse, Université de Toulouse, INSA, Toulouse, 31077, France · CMAP, CNRS, Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, 91120, France

  19. Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity

    May 6, 2026Saghar Zolfaghari, Yu Xie, Junfeng Yang +1Neural Surrogate ModelingMultifidelity Modeling

    Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA · School of Mechanical Engineering, University of Leeds, Leeds, UK

  20. Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks

    May 4, 2026Haizhou Wen, Elham Kiyani, Gang Li +3Surrogate ModelingSurrogate-Assisted Optimization

    Department of Mechanical Engineering, Clemson University, Clemson, SC 29634, USA · Division of Applied Mathematics, Brown University, Providence, RI 02912, USA · Center for Composite Materials, University of Delaware, Newark, DL 19716, USA +4

  21. Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations

    Apr 28, 2026Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi +9Machine Learning Interatomic PotentialsMultifidelity Modeling

    Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA · John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA · Robert Bosch LLC Research and Technology Center, Watertown, MA, USA

  22. Adaptive multi-fidelity optimization with fast learning rates

    Apr 17, 2026Come Fiegel, Victor Gabillon, Michal ValkoMulti-Fidelity OptimizationBlack-Box Optimization

    ´Ecole Normale Sup´erieure, Paris · Huawei R&D, UK · Inria Lille

  23. Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data

    Apr 15, 2026Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta +8Graph Foundation ModelsHigh-Performance Computing

    Computational Sciences and Engineering Division, Oak Ridge National Laboratory · Computer Science and Mathematics Division, Oak Ridge National Laboratory · National Center for Computational Sciences Division, Oak Ridge National Laboratory +2

  24. An Intelligent Fault Diagnosis Method for General Aviation Aircraft Based on Multi-Fidelity Digital Twin and FMEA Knowledge Enhancement

    Apr 3, 2026Zhihuan Wei, Yang Hu, Xinhang Chen +3Fault DiagnosisDigital Twins

    Hangzhou International Innovation Institute, Beihang University, Hangzhou, China · School of Mechanical Engineering, Zhejiang University, Hangzhou, China · School of Reliability and Systems Engineering, Beihang University, Beijing, China +1

  25. A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems

    Apr 2, 2026Kiran Yalamanchi, Shivam Barwey, Ibrahim Jarrah +1Computational Fluid DynamicsPDE Surrogate Modeling

    Transportation and Power Systems Division, Argonne National Laboratory, Lemont, IL, USA

  26. LGFNet: Local-Global Fusion Network with Fidelity Gap Delta Learning for Multi-Source Aerodynamics

    Mar 31, 2026Qinye Zhu, Yu Xiang, Jun Zhang +1Multi-Scale Feature FusionMultifidelity Modeling

    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

  27. Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data

    Mar 23, 2026Atticus Rex, Elizabeth Qian, David PetersonSurrogate ModelingMultifidelity Modeling

    School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA · School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, GA · Air Force Research Lab, Wright-Patterson AFB, Dayton, Ohio

  28. Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization

    Mar 17, 2026Isaac Robledo, Alberto Vilariño, Arnau Miró +3Multi-Fidelity OptimizationSurrogate Modeling

    Aerial Platforms Department Subdirectorate General for Aeronautical Systems Spanish National Institute for Aerospace Technology (INTA) · Department of Aerospace Engineering Universidad Carlos III de Madrid Madrid, Spain · TUAREG Research Group Universitat Politècnica de Catalunya (UPC) CASE Large-Scale Computational Fluid Dynamics Barcelona Supercomputing Centre (BSC) Barcelona, Spain

  29. Multifidelity Surrogate Modeling of Depressurized Loss of Forced Cooling in High-temperature Gas Reactors

    Mar 14, 2026Meredith Eaheart, Majdi I. RadaidehSurrogate ModelingMultifidelity Modeling

    Department of Nuclear Engineering and Radiological Science, University of Michigan, Ann Arbor, Michigan 48109 · Department of Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109

  30. Surrogates for Physics-based and Data-driven Modelling of Parametric Systems: Review and New Perspectives

    Mar 13, 2026Matteo Giacomini, Pedro DíezSurrogate ModelingReduced-Order Modeling

    Laboratori de Càlcul Numeric (LaCàN), E.T.S. de Ingeniería de Caminos, Canales y Puertos, Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain. · Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE), Barcelona, Spain.