System Identification

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Overview

System identification uses observations of a system, often alongside controlled inputs, to infer how it changes over time and how it will respond to new conditions. Approaches range from ARX and state-space or subspace methods to sparse equation discovery with SINDy, Koopman operators, neural ODEs, and physics-structured models such as Hamiltonian networks; Bayesian and active-learning methods help choose experiments and quantify uncertainty. These tools support prediction and control in settings including robotics, industrial processes, physical experiments, and systems observed through video. A central challenge is that accurate prediction on observed data does not guarantee that the underlying laws or parameters are identifiable, especially with sparse, noisy, partial, or weakly excited observations.

Latest papers

CardsList

157 papers19 papers in the last four weeks, down 10% on the four weeks before. 0.1% of all new papers.

  1. Optimal Centered Active Excitation in Linear System Identification

    Apr 7, 2026Kaito Ito, Alexandre ProutiereSample ComplexityActive Learning

    Department of Information Physics and Computing, The University of Tokyo, Tokyo 113-8654, Japan · Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm 114 28, Sweden

  2. Robust Learning of Heterogeneous Dynamic Systems

    Apr 7, 2026Shuoxun Xu, Zijian Guo, Brooke R. Staveland +2Dynamical SystemsRobust Estimation

    University of California at Berkeley · Zhejiang University · University of California at San Francisco

  3. A Robust SINDy Autoencoder for Noisy Dynamical System Identification

    Apr 6, 2026Kairui DingNonlinear System IdentificationSystem Identification

    Department of Mathematics, Columbia University

  4. Learning interacting particle systems from unlabeled data

    Apr 2, 2026Viska Wei, Fei LuSystem IdentificationNonlinear System Identification

    Department of Applied Mathematics and Statistics and Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218, USA. · Department of Mathematics, Johns Hopkins University, Baltimore, MD 21218, USA.

  5. Interpretable Physics Extraction from Data for Linear Dynamical Systems using Lie Generator Networks

    Mar 28, 2026Shafayeth Jamil, Rehan KapadiaStructure-Preserving Neural NetworksDynamical Systems

    Department of Electrical and Computer Engineering, University of Southern California

  6. Auto-differentiable data assimilation: Co-learning of states, dynamics, and filtering algorithms

    Mar 21, 2026Melissa Adrian, Daniel Sanz-Alonso, Rebecca WillettData AssimilationState Space Models

    School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 · Department of Statistics, The University of Chicago, Chicago, IL 60637 · Department of Computer Science, The University of Chicago, Chicago, IL 60637 +1

  7. Swim2Real: VLM-Guided System Identification for Sim-to-Real Transfer

    Mar 21, 2026Kevin Qiu, Kyle Walker, Mike Y. Michelis +2Robot System IdentificationSim-to-Real Transfer

    University of Warsaw · IDEAS NCBR · EPFL +2

  8. Achieving O~(1/ε)\widetilde{O}(1/ε) Sample Complexity for Bilinear Systems Identification under Bounded Noises

    Mar 21, 2026Hongyu Yi, Chenbei Lu, Jing YuParameter EstimationUncertainty Quantification

    Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA. · Cornell University AI for Science Institute, Cornell University, Ithaca, NY, USA.

  9. IRIS: A Real-World Benchmark for Inverse Recovery and Identification of Physical Dynamic Systems from Monocular Video

    Mar 17, 2026Rasul Khanbayov, Mohamed Rayan Barhdadi, Erchin Serpedin +1Parameter IdentifiabilitySystem Identification

    Hamad Bin Khalifa University · Texas A&M University

  10. Discovery of interaction and diffusion kernels in particle-to-mean-field multi-agent systems

    Mar 16, 2026Giacomo Albi, Alessandro Alla, Elisa CalzolaSystem Identification

    Department of Computer Science, University of Verona, Verona, Italy · Department of Mathematics, La Sapienza University of Rome, Rome, Italy

  11. Coordinate-Independent Robot Model Identification

    Mar 15, 2026Yanhao Yang, Ross L. HattonRobot Dynamics IdentificationRobot System Identification

    Collaborative Robotics and Intelligent Systems (CoRIS) Institute at Oregon State University, Corvallis, OR USA

  12. Disentangling Dynamical Systems: Causal Representation Learning Meets Local Sparse Attention

    Mar 15, 2026Markus W. Baumgartner, Anson Lei, Joe Watson +1System IdentificationParameter Identifiability

    Applied Artificial Intelligence Lab, Oxford Robotics Institute, Oxford, UK

  13. Flowcean - Model Learning for Cyber-Physical Systems

    Mar 12, 2026Maximilian Schmidt, Swantje Plambeck, Markus Knitt +4Cyber-Physical SystemsSystem Identification

    Hamburg University of Technology, Hamburg, Germany · Fraunhofer — Center for Maritime Logistics and Services CML, Hamburg, Germany · OFFIS — Institute for Information Technology, Oldenburg, Germany

  14. JEDI: Jointly Embedded Inference of Neural Dynamics

    Mar 11, 2026Anirudh Jamkhandi, Ali Korojy, Olivier Codol +2Latent Dynamics ModelingDynamical Systems

    Mila - Quebec AI Institute · Université de Montréal

  15. A neural operator for predicting vibration frequency response curves from limited data

    Mar 10, 2026D. Bluedorn, A. Badawy, B. E. Saunders +2Neural Surrogate ModelingNeural Operators

    Department of Mechanical and Aerospace Engineering, New Mexico State University, Las Cruces, NM 88003, USA. · Knight Foundation School of Computing and Information Science, Florida International University, Miami, FL 33199, USA. · Sandia National Laboratories, Albuquerque, NM 87123, USA.

  16. Unified Structural-Hydrodynamic Modeling of Underwater Underactuated Mechanisms and Soft Robots

    Mar 9, 2026Chenrui Zhang, Yiyuan Zhang, Yunfei Ye +3Robot System IdentificationUnderwater Robotics

    Department of Mechanical Engineering and Advanced Robotics Centre, National University of Singapore, Singapore. · Singapore-MIT Alliance for Research and Technology (SMART) Centre, 138602, Singapore.

  17. MOSIV: Multi-Object System Identification from Videos

    Mar 6, 2026Chunjiang Liu, Xiaoyuan Wang, Qingran Lin +9System IdentificationMaterials Property Prediction

    CMU · Georgia Tech · Harvard +4

  18. Random Dot Product Graphs as Dynamical Systems: Limitations and Opportunities

    Mar 5, 2026Giulio Valentino Dalla RivaLatent Dynamics ModelingDynamic Graph Learning

  19. Using the SEKF to Transfer NN Models of Dynamical Systems with Limited Data

    Mar 2, 2026Joshua E. Hammond, Tyler A. Soderstrom, Brian A. Korgel +1System IdentificationNeural Surrogate Modeling

    McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 East Dean Keeton St., Stop C0400, Austin, 78712, TX, United States · Energy Institute, The University of Texas at Austin, 2304 Whitis Ave., Stop C2400, Austin, 78712, TX, United States · Texas Materials Institute, The University of Texas at Austin, 204 E. Dean Keeton St., Stop C2201, Austin, 78712, TX, United States +2

  20. Latent Matters: Learning Deep State-Space Models

    Feb 26, 2026Alexej Klushyn, Richard Kurle, Maximilian Soelch +2State Space ModelsLatent Dynamics Modeling

    Technical University of Munich · AWS AI Labs · Machine Learning Research Lab, Volkswagen Group, Munich +1

  21. Dual-Regime Hybrid Aerodynamic Modeling of Winged Blimps With Neural Mixing

    Feb 25, 2026Xiaorui Wang, Hongwu Wang, Yue Fan +2Vehicle DynamicsSystem Identification

    The authors are with the Robotics and Control Laboratory, School of Advanced Manufacturing and Robotics, and the State Key Laboratory of Turbulence and Complex Systems, Peking University, Beijing, 100871, China

  22. Vid2Sid: Videos Can Help Close the Sim2Real Gap

    Feb 22, 2026Kevin Qiu, Yu Zhang, Marek Cygan +1Robot System IdentificationRobot Dynamics Identification

    University of Warsaw · IDEAS NCBR · EPFL +1

  23. PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting

    Feb 20, 2026Shubham Bhardwaj, Chandrajit BajajTime Series ForecastingPort-Hamiltonian Systems

    Department of Computer Science, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA

  24. Variational Grey-Box Dynamics Matching

    Feb 19, 2026Gurjeet Sangra Singh, Frantzeska Lavda, Giangiacomo Mercatali +1Physics-Informed Generative ModelingSystem Identification

  25. System Identification under Constraints and Disturbance: A Bayesian Estimation Approach

    Feb 18, 2026Sergi Martinez, Steve Tonneau, Carlos MastalliRobot System IdentificationSystem Identification

    Robot Motor Intelligence (RoMI) Lab, Heriot-Watt University, U.K. · Informatics School, University of Edinburgh, U.K.

  26. Uniform error bounds for quantized dynamical models

    Feb 17, 2026Abdelkader Metakalard, Fabien Lauer, Kevin Colin +1System IdentificationGeneralization Bounds

    CRAN, SYNALP · ∗Universit´e de Lorraine, CNRS, CRAN, F-54000 Nancy, France · ∗∗Universit´e de Lorraine, CNRS, LORIA, F-54000 Nancy, France +2

  27. Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics

    Dec 15, 2025Eugenio Varetti, Matteo Torzoni, Marco Tezzele +1System IdentificationDigital Twins

    MOX – Dipartimento di Matematica, Politecnico di Milano, Milan, 20133, Italy · Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Milan, 20133, Italy · Mathematics Department, Emory University, Atlanta, 30322, GA, United States

  28. Symmetric Linear Dynamical Systems are Learnable from Few Observations

    Dec 5, 2025Minh Vu, Andrey Y. Lokhov, Marc VuffrayDynamical SystemsParameter Estimation

    Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

  29. When is a System Discoverable from Data? Discovery Requires Chaos

    Nov 12, 2025Zakhar Shumaylov, Peter Zaika, Philipp Scholl +3Dynamical SystemsNonlinear System Identification

    University of Cambridge · LMU Munich and Munich Center for Machine Learning (MCML) · IBM Research

  30. Data-efficient Kernel Methods for Learning Hamiltonian Systems

    Sep 21, 2025Yasamin Jalalian, Mostafa Samir, Boumediene Hamzi +2Dynamical SystemsNonlinear System Identification

    Department of Computing and Mathematical Sciences, Caltech, CA, USA. · Beyond Limits, CA, USA. · The Alan Turing Institute, London, UK. +1