Reduced-Order Modeling

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Overview

Reduced-order modeling represents a high-dimensional system with a smaller set of variables so it can be simulated or controlled at lower computational cost. Common approaches include proper orthogonal decomposition and Galerkin projection, dynamic mode decomposition, balanced truncation, and operator inference; newer methods use autoencoders, neural operators, and learned latent dynamics. Researchers increasingly combine these tools with physical constraints, adaptive basis updates, data assimilation, and uncertainty estimates, across applications such as fluid flow, materials, combustion, and robotics. A central challenge is balancing compact representations and fast predictions against stability, accuracy over long time horizons, and reliable generalization to new conditions.

Latest papers

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97 papers10 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

  1. KROM: Kernelized Reduced Order Modeling

    Feb 27, 2026Aras Bacho, Jonghyeon Lee, Houman OwhadiPDE Surrogate ModelingReduced-Order Modeling

    Department of Computing and Mathematical Sciences, Caltech, CA, USA.

  2. Efficient Adaptation of ROMs for Unsteady Flows Using Data Assimilation

    Feb 26, 2026Ismaël Zighed, Andrea Nóvoa, Luca Magri +1Reduced-Order Modeling

    Institut Jean le Rond d’Alembert, Sorbonne Université · ISIR, Sorbonne Université · SCAI, Sorbonne Université +2

  3. Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach

    Feb 25, 2026Nathalie C. Pinheiro, Donghu Guo, Hannah P. Menke +4Surrogate ModelingPDE Surrogate Modeling

    Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ UK · Institute of GeoEnergy Engineering, Heriot-Watt University, Edinburgh, EH14 1AS UK · Department of Civil and Environmental Engineering, Imperial College London, London, SW7 2AZ UK +2

  4. Neural-POD: A Plug-and-Play Neural Operator Framework for Infinite-Dimensional Functional Nonlinear Proper Orthogonal Decomposition

    Feb 17, 2026Changhong Mou, Binghang Lu, Guang LinProper Orthogonal DecompositionOrthogonal Representation Learning

    Department of Mathematics and Statistics, Utah State University, 3900 Old Main Hill, Logan, UT, 84322, USA. · School of Electrical and Computer Engineering, Purdue University, 610 Purdue Mall, West Lafayette, IN, 47907, USA. · Department of Mathematics, Purdue University, 610 Purdue Mall, West Lafayette, IN, 47907, USA. +1

  5. Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference

    Dec 30, 2025Shane A. McQuarrie, Mengwu Guo, Anirban ChaudhuriActive LearningPartial Differential Equations

    Department of Mathematics, Brigham Young University · Centre for Mathematical Sciences, Lund University · Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin

  6. Hybrid coupling with operator inference and the overlapping Schwarz alternating method

    Nov 20, 2025Irina Tezaur, Eric Parish, Anthony Gruber +3PDE SolvingReduced-Order Modeling

    Sandia National Laboratories, Livermore, CA and Albuquerque, NM, USA · Rice University, Houston, TX, USA

  7. Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics

    Jul 21, 2025Alex Guo, Michael D. GrahamReduced-Order ModelingChaotic Time-Series Prediction

    Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison WI 53706, USA