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

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

  1. Learning the structure of open quantum systems

    Jun 29, 2026Laura Lewis, Ewin Tang, John WrightQuantum System IdentificationQuantum Machine Learning

    UC Berkeley

  2. Distributed Air-Gap Flux and Rotor-Current Fusion for Operating-Regime Identification in a 10-MW Kaplan Hydrogenerator

    Jun 26, 2026Eduardo Jr Piedad, Rafel Roig, Xavier Escaler +2System IdentificationMultimodal Sensor Fusion

    DOST–Advanced Science and Technology Institute, Quezon City 1101 Philippines · Universitat Politècnica de Catalunya, 08034 Barcelona, Spain

  3. Symplectic Neural Networks for learning Generalized Hamiltonians

    Jun 25, 2026Harsh Choudhary, Vyacheslav Kungurtsev, Chandan Gupta +2Dynamical SystemsSymplectic RNNs

    Computer Science Department, Indraprasth Institute of Information Technology, New Delhi, India 110020 · Athena Research and Innovation Center, Marousi, Greece 15125 · HSBC Holdings, Singapore

  4. RevengeBench: Reverse Engineering Code-Space Policies from Behavioral Experiments

    Jun 24, 2026Babak Rahmani, Sebastian Dziadzio, Joschka Strüber +2Opponent ModelingLLM-Based Program Synthesis

    Tübingen AI Center

  5. In-Context World Modeling for Robotic Control

    Jun 24, 2026Siyin Wang, Junhao Shi, Senyu Fei +4ICL for RoboticsWorld Model Learning

    1Fudan University · 2Shanghai Innovation Institute · 3Tongji University

  6. RigPI: Dynamic Parameter Identification of Rigid Body via VLM-Seeded Differentiable Simulation

    Jun 23, 2026Xincheng He, Rongrong Zhang, Wei Jiang +1Robot Dynamics IdentificationRobotic Manipulation

    Way Robotics

  7. LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search

    Jun 23, 2026Nikhil Abhyankar, Sha Li, Sanchit Kabra +3Nonlinear System IdentificationSystem Identification

    Virginia Tech

  8. How Should a Simulation-to-Reality Transfer Budget Be Spent?

    Jun 20, 2026Syed Hamzah Rizvi, Yash Vardhan TomarRobot Dynamics IdentificationDomain Randomization

    Purdue University West Lafayette, USA

  9. Eigenspace-Based Clustering for Personalized System Identification

    Jun 18, 2026Abdulmoneam Ali, Dipankar Maity, Ahmed ArafaClusteringUnsupervised Clustering

    Department of Electrical and Computer Engineering University of North Carolina at Charlotte, NC 28223

  10. A Differentiable Composite Approximation Framework for Autonomous Underwater Vehicle Maneuvering Modeling from Sea-Trial Data

    Jun 18, 2026Aobo Wang, Aifei Xia, Zihao Wang +1Dynamical SystemsUnderwater Robotics

    College of Shipbuilding Engineering, Harbin Engineering University, Harbin, 150001, China · China Academy of Aerospace Aerodynamics, Beijing, 100074, China · Institute of Artificial Intelligence, Shanghai University, Shanghai, 200444, China +1

  11. Optimal Ansatz-free Hamiltonian Learning In Situ

    Jun 17, 2026Taiqi Zhou, Weiyuan GongQuantum System IdentificationSystem Identification

    Department of Information Engineering, The Chinese University of Hong Kong. · John A. Paulson School of Engineering and Applied Sciences, Harvard University. · California Institute of Technology

  12. Learning Hybrid Biophysical Neuron Models with Neural ODEs

    Jun 15, 2026Jonas Beck, Michael Deistler, Dóra Viktória Molnár +2System IdentificationNeural ODEs

    Hertie Institute for AI in Brain Health, Tübingen, Germany · Tübingen AI Center, University of Tübingen, Tübingen, Germany · Machine Learning in Science, Excellence Cluster Machine Learning, University of Tübingen, Tübingen, Germany +2

  13. Regularized Machine Learning for System Identification of Ship Free-Running Manoeuvres from CFD-Based Synthetic Data: A Comparative Study

    Jun 15, 2026R. F. Suárez, J. C. Berndt, M. Abdel-MaksoudNonlinear System IdentificationSystem Identification

    Hamburg University of Technology (TUHH), Institute for Fluid Dynamics and Ship Theory (M8), Am Schwarzenberg-Campus 4 (C), 21073, Hamburg, Germany

  14. Closing the Approximation Gap in Simulation-free Latent SDEs

    Jun 15, 2026Henry D. Smith, Brian L. Trippe, Scott W. LindermanParameter EstimationStochastic Differential Equations

    Stanford University

  15. Identification of a Physics-Based Electrical Power Consumption Model for the Unitree G1 Humanoid Arm

    Jun 14, 2026Nestor N. Deniz, Sebastian Vega, Simon Parsons +1RoboticsSystem Identification

    Engineering Department at Harper Adams University, Newport, Shropshire TF10 8NB, UK · Lincoln Institute for Agri-Food Technology and Lincoln Centre for Autonomous Systems

  16. SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning

    Jun 11, 2026Yash Vardhan Tomar, Dheeraj PeddireddyReinforcement LearningQuantum System Identification

    Purdue University West Lafayette, United States

  17. How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit

    Jun 10, 2026Ana Larrañaga, Urban Fasel, Steven L. BruntonSparse Identification of Nonlinear DynamicsPDE Discovery

    Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, United States · NSF AI Institute in Dynamic Systems, University of Washington, Seattle, WA 98195, United States · Department of Aeronautics, Imperial College London, SW7 2AZ, United Kingdom

  18. Embedding Hybrid Systems into Continuous Latent Vector Fields

    Jun 9, 2026Sangli Teng, Hang Liu, Koushil SreenathLatent Dynamics ModelingSystem Identification

    University of California, Berkeley, CA, USA · University of Michigan, Ann Arbor, MI, USA

  19. Learning Explicit Behavioral Models with Adaptive Questions and World-Model Probes

    Jun 5, 2026Hikaru Shindo, Yu Deng, Teng Cao +5Reinforcement LearningWorld Models

    1Artificial Intelligence and Machine Learning Lab, Technical University of Darmstadt, Germany · Department of Computer Science, Technical University of Darmstadt, Germany · 2Hessian Center for Artificial Intelligence (hessian.AI), Germany +3

  20. Learning Control-Affine Reduced-Order Models via Autoencoders

    Jun 3, 2026Ali Mjalled, Martin MönnigmannNonlinear System IdentificationSystem Identification

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

  21. Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data

    May 29, 2026Sean Reiter, Steffen W. R. WernerSystem IdentificationReduced-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.

  22. BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control

    May 28, 2026Felix Koch, Thomas Krug, Fabian Raisch +2Synthetic Data GenerationSystem Identification

    Technical University of Applied Sciences Rosenheim Rosenheim, Germany · Technical University of Munich Munich, Germany · Karlsruhe Institute of Technology Karlsruhe, Germany

  23. Physics from Video: Identifiability of Time-Invariant Second-Order ODEs under Minimal Trajectory Conditions

    May 27, 2026Yuanyuan Wang, Wenjie Wang, Kun Zhang +1Parameter IdentifiabilityLatent Dynamics Modeling

    Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · University of Melbourne, Melbourne, Australia · Carnegie Mellon University, Pittsburgh, USA

  24. EigeNet: Geometry-Informed Multi-Modal Learning for Few-shot Novel View RIR Prediction

    May 27, 2026Chong Jing, Zitong Lan, Junan Zhang +1Multi-View LearningFew-Shot Learning

    The Chinese University of Hong Kong, Shenzhen · University of Pennsylvania

  25. PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers

    May 25, 2026Koyo Fujii, Luis Figueredo, Praminda Caleb-Solly +4Physical ReasoningRobotic Manipulation

    School of Computer Science, University of Nottingham, Nottingham, United Kingdom · Dept. of Industrial Engineering, University of Bologna, Bologna, Italy

  26. DiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinking

    May 25, 2026Matt L. Wiemann, Lindsay M. Smith, Peter Melchior +4Physical ReasoningScientific Discovery

    Princeton University · Work done during internship at NYU/Polymathic AI. · Boston University +3

  27. EMMA: Extracting Multiple physical parameters from Multimodal Data

    May 21, 2026Farhat Shaikh, Ayan Banerjee, Sandeep GuptaParameter EstimationLatent Dynamics Modeling

    IMPACT Lab, School of Computing & Augmented Intelligence (SCAI) Arizona State University, Tempe, AZ

  28. Time-Dependent PDE-Constrained Optimization via Weak-Form Latent Dynamics

    May 20, 2026April Tran, Terry Haut, David Bortz +1Latent Dynamics ModelingPDE 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

  29. Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems

    May 19, 2026Julian Langschwert, Georg Schaefer, Jakob Rehrl +2System Identification

    Josef Ressel Centre for Intelligent and Secure Industrial Automation, Salzburg University of Applied Sciences, Salzburg, Austria · Paris Lodron University of Salzburg, Salzburg, Austria

  30. Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot

    May 18, 2026Harald Minde Hansen, Bjørn Kåre Sæbø, Kristin Y. Pettersen +2Robot Dynamics IdentificationNonlinear System Identification

    European Organization for Nuclear Research (CERN), Switzerland · Department of Engineering Cybernetics, Norwegian University of Science and Technology, NTNU, NO-7491 Trondheim, Norway