Reduced-Order Modeling

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8 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

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Learning Control-Affine Reduced-Order Models via Autoencoders
Jun 3, 2026Ali Mjalled, Martin MönnigmannNonlinear System Identification System Identification
Automatic Control and Systems Theory Ruhr-Universität Bochum Universitätstraße 150, 44801, Bochum
Learning symplectic model reduction based on a approximation theorem of symplectic embeddings
Jun 3, 2026Liyi Feng, Yifa Tang, Yulin Xie +2Dynamical Systems Autoencoders
School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, China · State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China · Department of Mathematics, National University of Singapore, 119076, Singapore
Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data
May 29, 2026Sean Reiter, Steffen W. R. WernerSystem Identification Reduced-Order Modeling
Courant Institute School of Mathematics, Computing, and Data Science, New York University, New York, NY 10012, USA. · Department of Mathematics, Division of Computational Modeling and Data Analytics, and National Security Institute, Virginia Tech, Blacksburg, VA 24061, USA.
Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor
May 28, 2026Minseo Lee, Seongmin Oh, Chaehyeon Song +5PDE Surrogate Modeling Reduced-Order Modeling
Division of Advanced Nuclear Engineering, POSTECH, 772026 Cheongam-Ro, Pohang-si, 37673, Gyeongbuk-do, Republic of Korea · Department of Quantum System Engineering, Jeonbuk National University, 567 Baekje-daero, Jeonju-si, 54896, Jeollabuk-do, Republic of Korea · Department of Nuclear Engineering, Hanyang University, Wangsimni-ro, Seongdong-gu, Seongdong-gu, 04763, Seoul, Republic of Korea +1
FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes
May 28, 2026Donglai Xiang, Vismay Modi, Rishit Dagli +5Hyperelasticity Reduced-Order Modeling
NVIDIA · 1NVIDIA · University of Toronto
Uncertainty-aware Multi-fidelity Closure via Conditional Normalizing Flows
May 27, 2026Jice Zeng, Shady E. Ahmed, David Barajas-Solano +1Uncertainty Quantification Multifidelity Modeling
Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, Richland, 99352, WA, USA
History-aware adaptive reduced-order models via incremental singular value decomposition
May 27, 2026Amirpasha Hedayat, Ali Mohaghegh, Laura Balzano +2Reduced-Order Modeling Singular Value Decomposition
Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, USA · Department of Aerospace Engineering, University of Kansas, Lawrence, KS, USA · Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
CFDTwin: An open-source GUI and Python toolkit for POD-NN surrogate modeling of ANSYS Fluent simulations
May 26, 2026Daniel Curl, Han HuPDE Surrogate Modeling Proper Orthogonal Decomposition
Department of Mechanical Engineering, University of Arkansas, Fayetteville, AR 72701, USA
Time-Dependent PDE-Constrained Optimization via Weak-Form Latent Dynamics
May 20, 2026April Tran, Terry Haut, David Bortz +1Latent Dynamics Modeling PDE Surrogate Modeling
Department of Applied Mathematics, University of Colorado, Boulder, CO, 80309, USA · Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, 94550, CA, USA
The impact of observation density on Bayesian inversion of latent dynamics in shock-dominated flows
May 18, 2026Bipin Tiwari, Muhammad Abid, Omer SanNeural Surrogate Modeling Uncertainty Quantification
Department of Mechanical and Aerospace Engineering, University of Tennessee, Knoxville Knoxville, TN 37996, USA.
A Unified Framework for Structured Flow Modeling: From Representation to Verification and Model Discovery
May 18, 2026Diego CasadeiDynamical Systems Hodge Decomposition
Fly High Engineering AG, Badenerstrasse 13, 5200 Brugg AG, Switzerland
pyforce-1.0.0: Python Framework for data-driven model Order Reduction of multi-physiCs problEms
May 18, 2026Stefano Riva, Yantao Luo, Carolina Introini +1Reduced-Order Modeling Physics-Based Simulation
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
Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics
May 8, 2026Hamidreza Eivazi, Henning WesselsStructural Mechanics Hyperelasticity
Division Data-Driven Modeling of Mechanical Systems, Institute of Applied Mechanics, Technische Universität Braunschweig, Pockelsstr. 3, 38106 Braunschweig, Germany
PhySPRING: Structure-Preserving Reduction of Physics-Informed Twins via GNN
May 8, 2026Yixiong Jing, Xingyuan Chen, Guangming Wang +3Graph Neural Networks Robotics Simulation
University of Cambridge · Shanghai Jiao Tong University
Discrete Elastic Ribbons: A Unified Discrete Differential Geometry Framework for One-Dimensional Energy Models
May 7, 2026Shivam Kumar Panda, M Khalid JawedStructural Mechanics Reduced-Order Modeling
University of California, Los Angeles, 420 Westwood Plaza, Los Angeles, 90095, CA, United States of America
Reduced-order Neural Modeling with Differentiable Simulation for High-Detail Tactile Perception
May 6, 2026Yuhu Guo, Zhikai Shen, Jiasheng Qu +4Neural Surrogate Modeling Material Point Method
The University of Manchester, UK · The Chinese University of Hong Kong, Hong Kong SAR, China · University of Leeds, UK +1
Constrained Extreme Gradient Boosting for Adapting Reduced-Order Models
May 5, 2026Melika Baghi, Xiao Liu, Kamran PaynabarGradient-Boosted Decision Trees Reduced-Order Modeling
Georgia Institute of Technology, Atlanta, GA 30332, USA
Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction
May 5, 2026Henning Schwarz, Pyei Phyo Lin, Jens-Peter M. Zemke +1Neural Surrogate Modeling Implicit Neural Representations
Institute for Fluid Dynamics and Ship Theory, Hamburg University of Technology, Am Schwarzenberg-Campus 4, D-21073 Hamburg, Germany · Institute of Mathematics, Hamburg University of Technology, Am Schwarzenberg-Campus 3, D-21073 Hamburg, Germany
Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning
Apr 27, 2026Arielle Sanford, Andrew T. Kamen, Frederic T. Chong +1Reduced-Order Modeling Data-Efficient Learning
Department of Computer Science, University of Chicago, Chicago, IL 60637 · Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637 · Johns Hopkins Applied Physics Laboratory, Laurel, MD 20723
Conditional Score-Based Modeling of Effective Langevin Dynamics
Apr 27, 2026Ludovico T. GiorginiLangevin Dynamics Model Calibration
Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Machine learning moment closure models for the radiative transfer equation IV: enforcing symmetrizable hyperbolicity in two dimensions
Apr 22, 2026Juntao HuangReduced-Order Modeling Scientific ML
Department of Mathematical Sciences, University of Delaware, Newark, DE, 19716, USA.
Physics-Guided Dimension Reduction for Simulation-Free Operator Learning of Stiff Differential-Algebraic Systems
Apr 21, 2026Huy Hoang Le, Haoguang Wang, Christian Moya +2Reduced-Order Modeling Deep Operator Networks
School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 USA · Department of Mathematics, Purdue University, West Lafayette, IN 47907 USA · Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA
HALO: Hybrid Auto-encoded Locomotion with Learned Latent Dynamics, Poincaré Maps, and Regions of Attraction
Apr 20, 2026Blake Werner, Sergio A. Esteban, Massimiliano De Sa +2Dynamical Systems Humanoid Robot Locomotion
Caltech, Pasadena, CA, USA · NC State University, Raleigh, NC, USA
A convolutional autoencoder and neural ODE surrogate modeling framework applied to transient counterflow flames
Mar 16, 2026Mert Yakup Baykan, Weitao Liu, Mohammad Rafi Malik +4Autoencoders PDE Surrogate Modeling
Department of Aerospace Engineering, KAIST, Daejeon, Republic of Korea · Clean Energy Research Platform (CERP), KAUST, Thuwal, Saudi Arabia · Institute for Reactive Flows (IRST), University of Stuttgart, Pfaffenwaldring 31, 70569 Stuttgart, Germany
Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems
Mar 13, 2026Nicolò Botteghi, Silke Glas, Christoph BruneAutoencoders PDE Surrogate Modeling
1MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milano, Italy · Department of Applied Mathematics, University of Twente, Enschede, Netherlands
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
Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach
Feb 25, 2026Nathalie C. Pinheiro, Donghu Guo, Hannah P. Menke +4Surrogate Modeling PDE 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
Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference
Dec 30, 2025Shane A. McQuarrie, Mengwu Guo, Anirban ChaudhuriActive Learning Reduced-Order Modeling
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
Hybrid coupling with operator inference and the overlapping Schwarz alternating method
Nov 20, 2025Irina Tezaur, Eric Parish, Anthony Gruber +3PDE Solving Reduced-Order Modeling
Sandia National Laboratories, Livermore, CA and Albuquerque, NM, USA · Rice University, Houston, TX, USA
A Dynamic Toolkit for Transmission Characteristics of Precision Reducers with Explicit Contact Geometry
Date pendingJiacheng Miao, Chao Liu, Qiliang Wang +2Dynamical Systems Multibody Dynamics
Intelligent Equipment Division, CRRC Qishuyan Institute Co., Ltd, Changzhou, 213011, China · School of Mechanical Engineering, Dalian Jiaotong University, Dalian, 116028, China · Research Institute, CRRC Qishuyan Institute Co., Ltd, Changzhou, 213011, China

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Learning Control-Affine Reduced-Order Models via Autoencoders

Ali Mjalled, Martin Mönnigmann

Automatic Control and Systems Theory Ruhr-Universität Bochum Universitätstraße 150, 44801, Bochum

Nonlinear System Identification System Identification Reduced-Order Modeling State Space Models

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We present in this paper a framework for the identification of control-affine reduced-order models (ROMs). The proposed method utilizes autoencoders (AEs) to transform the high-dimensional states, and potentially the high-dimensional inputs, into reduced latent ones suitable for control-affine state-space dynamics. This is achieved by simultaneous training of the AE and the state-space model. In addition, we extend the discrete ROM formulation to a sequence-based model, which processes state and input histories to improve prediction accuracy while preserving the control-affine structure. We motivate our framework by applying feedback linearization to the derived models, and we present guidelines for its efficient use. The proposed framework is assessed on two numerical examples and its performance is compared to a baseline model, where the AE identifies a latent space with linear state-space dynamics. The assessment involves evaluating the prediction accuracy of the ROM on test data and its effectiveness in controlling the system to a desired state or trajectory.

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