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. Predicting Sparse Observables in Parameterized Unsteady Flows Using Coarse-Mesh Graph Neural Networks

    Oct 27, 2026Brener Ramos, William R. Wolf, Tarcísio C. D. Oliveira +1PDE Surrogate ModelingReduced-Order Modeling

    School of Mechanical Engineering, University of Campinas, Campinas, São Paulo, Brazil · Department of Mechanical, Materials, and Aerospace Engineering, Illinois Institute of Technology, Chicago, Illinois, USA

  2. Balanced Active Subspaces for Optimal Control via Trajectory Optimization

    Oct 26, 2026Emmanuel S. Ameh, Samuel E. OttoOptimal ControlReduced-Order Modeling

    Department of Chemical and Biomolecular Engineering · Department of Mechanical and Aerospace Engineering

  3. Model reduction in time: Learning flow maps for reducing stochastic dynamical systems in time

    Oct 26, 2026Shreya Jha, Benjamin PeherstorferReduced-Order ModelingFlow Map Learning

    Courant Institute School of Mathematics, Computing, and Data Science, New York University · Institute of Mathematics, EPFL

  4. Neural Eigenfluid Bases: Spectral Objectives, Gauge, and Topology

    Oct 24, 2026Baiming Zhang, Shiying Xiong, Zhecheng WangReduced-Order ModelingComputational Fluid Dynamics

    Zhejiang University · University of Toronto

  5. Greedy Orthogonal Polynomial Manifolds with Lipschitz Control for Dimensionality Reduction and Reduced Modeling

    Oct 24, 2026Tomoki Koike, Benjamin Peherstorfer, Elizabeth QianDimensionality ReductionManifold Learning

    School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA · Institute of Mathematics, EPFL, Lausanne, Switzerland · School of Aerospace Engineering and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

  6. Structure-preserving reduced-order models of thin panels in supersonic and hypersonic flow

    Oct 22, 2026Miguel A. AguiloReduced-Order ModelingPhysics-Informed ML

    Morphorm LLC, 100 Sun Avenue Northeast Suite 650, Albuquerque, NM 87109-4670, USA

  7. Hyperreduction accelerates finite element model updating for the discovery of neural materials

    Oct 22, 2026Hagen Holthusen, Matthias Knipper, Kevin LinkaFinite Element MethodReduced-Order Modeling

    Computational Mechanics in Medicine, Institute of Applied Medical Engineering, RWTH Aachen University, Pauwelsstraße 20, 52074 Aachen, Germany

  8. Norm-optimal model order reduction in reproducing kernel Hilbert spaces

    Oct 20, 2026Tamás Dózsa, Andrea Angino, Rolf Krause +1Reduced-Order ModelingReproducing Kernel Hilbert Spaces

  9. PhysSkin++: Real-Time and Generalizable Physics-Based Animation via Self-Supervised Neural Skinning

    Oct 19, 2026Yuanhang Lei, Baofei Liu, Xingxuan Li +9Physics-Based SimulationPhysics-Informed ML

    State Key Laboratory of CAD&CG, Zhejiang University · Ant Group, Hangzhou 310000, China · Beijing Institute for General Artificial Intelligence (BIGAI) +2

  10. Null Controls Are Necessary but Not Sufficient: Evaluating Acceptance Guards for Scientific Discovery and Interpretability

    Oct 15, 2026Tianyu WuScientific DiscoveryAutomated Evaluation

    Project Aether

  11. Latent-Lagrangian Neural Networks for Reduced Order Modeling of Non-autonomous Nonlinear Dynamical Systems

    Oct 3, 2026Anand Kumar Agrawal, Anders ThorinLagrangian Neural NetworksReduced-Order Modeling

    Université Paris-Saclay, CEA List, Palaiseau, France

  12. From Redundancy to Minimality: Fixed-Point-Guided Hierarchical Reduction of Learned Piecewise-Linear Dynamics

    Oct 1, 2026Hiroto Tamura, Gouhei TanakaDynamical SystemsNonlinear System Identification

    Graduate School of Informatics, Osaka Metropolitan University, Osaka, Japan · International Research Center for Neurointelligence (IRCN), The University of Tokyo, Tokyo, Japan · Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan

  13. Evaluating Hybrid Quantum-Classical Models for Reduced-Order Brain Deformation Dynamics

    Sep 30, 2026Tao Liu, Ge He, Dongyu Liang +1Time Series ForecastingProper Orthogonal Decomposition

    Lawrence Technological University · North Carolina State University

  14. SMORE: Stability-Promoting Mesh-Agnostic Model Reduction for Time-Dependent PDEs

    Sep 27, 2026Yangyuan Li, Weichao Li, Shaowu PanPDE Surrogate ModelingReduced-Order Modeling

    Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute 110 8th Street, Troy, NY 12180, USA

  15. Reduced Cartesian Kinetostatics for Tendon-Driven Continuum Robots: Residual-Stabilized Full-Shape Propagation

    Sep 24, 2026Ke Wu, Fangju Yang, Xiaohui Zhang +3Continuum Robot Shape EstimationContinuum Robot Control

  16. Periodic Neural Mapping for Unsteady Rotor-Blade Pressure and Aeroelastic Load Prediction

    Sep 18, 2026Lionel Salesses, Joachim Dominique, Tariq Benamara +2Reduced-Order ModelingNeural Operators

    Cenaero, Gosselies, Belgium · Safran Helicopter Engines, Bordes, France

  17. Domain-decomposed Evolutional Deep Neural Network with Random Features for Transient Pressure Diffusion with Discontinuous and High-Contrast Coefficients

    Sep 17, 2026Peiqi Li, Jie Chen, Hui Zhang +1Porous Media FlowDomain Decomposition Methods

    Department of Applied Mathematics, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China · Department of Mathematical Sciences, University of Liverpool, Liverpool, L69 3BX, United Kingdom

  18. 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 · Sandia National Laboratories

  19. High-Fidelity Digital Twin Data Models by Randomized Dynamic Mode Decomposition and Deep Learning with Applications in Fluid Dynamics

    Sep 15, 2026Diana A. BistrianNeural Surrogate ModelingReduced-Order Modeling

    University Politehnica Timisoara, Department of Electrical Engineering and Industrial Informatics, Romania

  20. Improving Reduced-Order Rotating Detonation Engine Models with Data Assimilation and Machine Learning

    Sep 14, 2026Ashwin Suriyanarayanan, Romit MaulikData AssimilationReduced-Order Modeling

    School of Mechanical Engineering, Purdue University, West Lafayette, IN-47906

  21. Linearized PINN with pretrained nonlinear layers

    Sep 14, 2026Wenhao Chen, Alexandre M. TartakovskyEfficient Neural Network InferencePDE Solving

    Civil and Environmental Engineering Department University of Illinois Urbana-Champaign Urbana, IL 61801, USA · Pacific Northwest National Laboratory Richland, WA 99352, USA

  22. Floquet Fibre Geometry and Higher-Order Reduced Coordinates for Off-Manifold Transients near Nonlinear Aeroelastic Flutter

    Sep 13, 2026Puxue TanDynamical SystemsReduced-Order Modeling

    William Wright Technology Centre (W-Tech) School of Mechanical & Aerospace Engineering Queen’s University Belfast 50 Malone Road, Belfast, BT9 5BS, United Kingdom

  23. Two-Scale Localized PCA-Net: Coarse-Global and Local-Residual Representations for Artifact-Reduced PDE Operator Learning

    Sep 7, 2026Mrigank Dhingra, Jordan Stout, Omer SanPrincipal Component AnalysisReduced-Order Modeling

    Department of Mechanical & Aerospace Engineering University of Tennessee Knoxville, Tennessee 37917, USA · Department of Mathematics and Statistics Boston University Boston, Massachusetts 02215, USA

  24. Real-Time Monitoring of MHD Liquid Metal Flows with Shallow Recurrent Decoders

    Aug 28, 2026Claudio Scardino, Stefano Riva, Carolina Introini +4MagnetohydrodynamicsReduced-Order Modeling

    Energy Department, CeSNEF - Nuclear Engineering Division, Politecnico di Milano, Milano · Autodesk Research, 6 Agar Street, London, WC2R 0QE, UK · Argonne National Laboratory, Lemont, IL, 60439, United States of America +1

  25. ROMNet: a hybrid reduced order modeling and machine learning approach to waveform inversion

    Aug 25, 2026Liliana Borcea, Alexander Mamonov, Kui Ren +2Inverse ProblemsPDE Inverse Problems

    Applied Physics and Applied Mathematics, Columbia University, New York, NY, 10027. · Department of Mathematics, University of Houston, Houston, TX 77204-3008. · Department of Mathematics, University of Maryland, College Park, MD 20742.

  26. Quantum Reservoir Computing with Physics-Informed Correction for Reduced-Order PDE Forecasting

    Aug 24, 2026Krishna Bhatia, Harsh, Shalini DevendrababuQuantum Reservoir ComputingReduced-Order Modeling

    QuantumAI Lab, Fractal Analytics, Mumbai, India · Department of Physics, Indian Institute of Technology, Jodhpur, India

  27. Learning Reduced-Order Dynamics with Singularity via Latent-Augmented Neural Ordinary Differential Equations

    Aug 22, 2026Xiaorui Wang, Yu Zhou, Wenjie Mei +3Reduced-Order ModelingNeural ODEs

    Lanzhou University of Technology, School of Automation and Electrical Engineering, Lanzhou, 730050, Gansu, China · Hiroshima University, Graduate School of Advanced Science and Engineering, Higashihiroshima, 739-0046, Hiroshima, Japan · Nanjing University - Suzhou Campus, School of Robotics and Automation, Suzhou, 215163, Jiangsu, China +1

  28. Learning the Right Abstraction: Neural Reduced Dynamics for Complex Robot Control

    Aug 19, 2026Harry Zhang, Dan NegrutRobotic ControlRobot Dynamics Learning

    Department of Mechanical Engineering, University of Wisconsin–Madison, Madison, 53706, WI, USA

  29. Physics-informed reduced-order modelling with equivariant spectral submanifolds

    Aug 4, 2026Georg MaierhoferReduced-Order ModelingScientific ML

    Department of Applied Mathematics and Theoretical Physics · University of Cambridge, UK

  30. Conformal risk control for model-form uncertainty in parametric non-intrusive reduced-order models

    Aug 4, 2026Edgar Jaber, Rémy Vallot, Thibault Dairay +1Uncertainty QuantificationPDE Surrogate Modeling

    CB, ENS Paris Saclay · Université Paris-Saclay, CNRS, ENS Paris-Saclay, Centre Borelli, Gif-sur-Yvette, 91190, France · CB, Michelin +3