Latent Dynamics Modeling

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Overview

Latent dynamics modeling studies how to represent a changing system with hidden states and learn how those states evolve from observations such as video, sensor streams, or sparse measurements. Common approaches include autoencoders with state-space models, neural ODEs and SDEs for continuous-time or stochastic change, Koopman operators for approximately linear evolution, and JEPA-style world models for action-conditioned prediction. These models support forecasting and control in robotics and physical simulation, as well as analysis of biological, clinical, and neural time series. A central challenge is that good one-step prediction or reconstruction does not guarantee reliable long-horizon rollouts, useful control, or recovery of meaningful physical mechanisms; work therefore explores stability, identifiability, and physics-based structure while balancing these constraints against model flexibility.

Latest papers

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254 papers48 papers in the last four weeks, up 92% on the four weeks before. 0.3% of all new papers.

  1. Koopman-Based Nonlinear Identification and Model Predictive Control of a Turbofan Engine

    Apr 2, 2026David GrasevAdaptive MPCExtended Dynamic Mode Decomposition

    Department of Aviation Technology, University of Defence, Brno, Czech Republic.

  2. LAtent Phase Inference from Short time sequences using SHallow REcurrent Decoders (LAPIS-SHRED)

    Apr 1, 2026Yuxuan Bao, Xingyue Zhang, J. Nathan KutzSpatiotemporal ForecastingLatent Dynamics Modeling

    Department of Applied Mathematics, University of Washington. · School of Environmental and Forest Sciences, University of Washington. · Autodesk Research, London, UK.

  3. Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics

    Apr 1, 2026Sandeep Kumar Samota, Reema Gupta, Snehashish ChakravertyLatent Dynamics ModelingTime Series Forecasting

    Department of Mathematics, National Institute of Technology, Rourkela, India · Poverty Alleviation Research Centre, National Institute of Technology, Rourkela, India

  4. Koopman Operator Identification of Model Parameter Trajectories for Temporal Domain Generalization (KOMET)

    Mar 27, 2026Randy C. Hoover, Jacob James, Paul May +1Extended Dynamic Mode DecompositionDrifting Models

    Department of Electrical Engineering and Computer Science (EECS), South Dakota Mines, Rapid City, SD 57702, USA · Data Science and Engineering Program within EECS, South Dakota Mines, Rapid City, SD 57702, USA · Department of Mathematics, South Dakota Mines, Rapid City, SD 57702, USA

  5. Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learning

    Mar 24, 2026Chao Han, Stefanos Ioannou, Luca Manneschi +4Model-Based RLLatent Dynamics Modeling

    School of Computer Science, The University of Sheffield, UK · Cancer Research UK National Biomarker Centre, The University of Manchester, UK · School of Chemical, Materials and Biological Engineering, The University of Sheffield, UK +2

  6. Conditionally Identifiable Latent Representation for Multivariate Time Series with Structural Dynamics

    Mar 24, 2026Minkey Chang, Jae-Young KimLatent Variable ModelsLatent Dynamics Modeling

  7. Behavioral Heterogeneity as Quantum-Inspired Representation

    Mar 24, 2026Mohammad Elayan, Wissam KontarState Representation LearningLatent Dynamics Modeling

    University of Nebraska–Lincoln, Lincoln, NE, USA

  8. Generalizing Dynamics Modeling More Easily from Representation Perspective

    Mar 24, 2026Yiming Wang, Zhengnan Zhang, Genghe Zhang +7Dynamical SystemsLatent Dynamics Modeling

    College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China · College of Software and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China · School of Computing, Belfast, Northern Ireland, UK +1

  9. Evaluating Factor-Wise Auxiliary Dynamics Supervision for Latent Structure and Robustness in Simulated Humanoid Locomotion

    Mar 22, 2026Chayanin ChamachotLatent Dynamics ModelingDisentangled Representation Learning

    Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand

  10. Structured Latent Dynamics in Wireless CSI via Homomorphic World Models

    Mar 20, 2026Salmane Naoumi, Mehdi Bennis, Marwa ChafiiLatent Dynamics ModelingChannel State Prediction

    NYU WIRELESS, NYU Tandon School of Engineering, New York, USA. · Center for Wireless Communications, University of Oulu, Finland. · Engineering Division, New York University (NYU), Abu Dhabi, UAE.

  11. Accurate Open-Loop Control of a Soft Continuum Robot Using Visually Learned Latent Dynamics

    Mar 20, 2026Henrik Krauss, Johann Licher, Naoya Takeishi +2Robotic ControlContinuum Robot Control

    Department of Advanced Interdisciplinary Studies, The University of Tokyo, Tokyo, 153-8904, Japan. · Institute of Assembly Technology and Robotics, Leibniz University Hannover, 30823 Germany. · Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan.

  12. Identifying Latent Actions and Dynamics from Offline Data via Demonstrator Diversity

    Mar 18, 2026Felix SchurLatent Action LearningLatent Dynamics Modeling

    Department of Mathematics, ETH Zürich

  13. Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models

    Mar 16, 2026Pranaya Jajoo, Harshit Sikchi, Siddhant Agarwal +3Behavioral Foundation ModelsZero-Shot RL

    Department of Computing Science, University of Alberta, Canada · Alberta Machine Intelligence Institute (Amii) · The University of Texas at Austin +2

  14. Parameterized Temporal Extrapolation of PDEs via Disentangled Latent Dynamics Manifold Fusion

    Mar 13, 2026Zhangyong Liang, Jihong Zhu, Huanhuan GaoLatent Dynamics ModelingPDE Solving

    National Center for Applied Mathematics, Tianjin University, Tianjin, 300072, PR China · School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China · School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130025, China

  15. Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows

    Mar 12, 2026David Baumgartner, Eliezer de Souza da Silva, Iñigo UrteagaNormalizing FlowsLatent Dynamics Modeling

    Department of Computer Science, Norwegian University of Science and Technology, Norway · BCAM — Basque Center for Applied Mathematics, Spain · Department of Informatics Engineering, University of Coimbra, Portugal +2

  16. Learn Structure, Adapt on the Fly: Multi-Scale Residual Learning and Online Adaptation for Aerial Manipulators

    Mar 12, 2026Samaksh Ujjawal, Naveen Sudheer Nair, Shivansh Pratap Singh +3Aerial RoboticsTest-Time Adaptation

    1Robotics Research Center, IIIT Hyderabad, India. · Department of Computer Science, University of Manchester, UK. · 3Newcastle University, UK.

  17. World Model for Battery Degradation Prediction Under Non-Stationary Aging

    Mar 11, 2026Kai Chin Lim, Khay Wai SeeLithium-Ion BatteriesRUL Estimation

    Independent Researcher, Australia

  18. 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

  19. Generative prediction of laser-induced rocket ignition with dynamic latent space representations

    Mar 8, 2026Tony Zahtila, Ettore Saetta, Murray Cutforth +3Neural Surrogate ModelingPDE Surrogate Modeling

    Center for Turbulence Research, Stanford University, USA · University of Naples Federico II, Italy

  20. Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part II

    Mar 8, 2026Yi Tian, Kaiqing Zhang, Russ Tedrake +1Linear Quadratic RegulatorState Representation Learning

    Massachusetts Institute of Technology · University of Maryland, College Park · Technical University Munich

  21. LiveWorld: Simulating Out-of-Sight Dynamics in Generative Video World Models

    Mar 7, 2026Zicheng Duan, Jiatong Xia, Zeyu Zhang +7Video World ModelsWorld Models

    Adelaide University · The Australian National University · Monash University +2

  22. Latent Autoencoder Ensemble Kalman Filter for Nonlinear Data assimilation

    Mar 6, 2026Xin T. Tong, Yanyan Wang, Liang YanData AssimilationEnsemble Kalman Filtering

    Department of Mathematics, National University of Singapore, 119076, Singapore · School of Mathematics, Southeast University, Nanjing 210096, China

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

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

  24. Behavior-dLDS: A decomposed linear dynamical systems model for neural activity partially constrained by behavior

    Mar 5, 2026Eva Yezerets, En Yang, Misha B. Ahrens +1Latent Dynamics ModelingDynamical Systems

    Department of Biomedical Engineering Center for Imaging Science Johns Hopkins University · Department of Biology University of North Carolina · Janelia Research Campus Howard Hughes Medical Institute

  25. BLINK: Behavioral Latent Modeling of NK Cell Cytotoxicity

    Mar 5, 2026Iman Nematollahi, Jose Francisco Villena-Ossa, Alina Moter +6Toxicity DetectionLatent Dynamics Modeling

    Department of Computer Science, University of Freiburg, Freiburg, Germany · Institute for Transfusion Medicine and Gene Therapy, University Medical Center Freiburg, Freiburg, Germany · Goethe University, Department of Pediatrics, Experimental Immunology and Cell Therapy, Frankfurt am Main, Germany +2

  26. On Geometry Regularization in Autoencoder Reduced-Order Models with Latent Neural ODE Dynamics

    Mar 3, 2026Mikhail OsipovReduced-Order ModelingAutoencoders

    Independent Researcher, Italy

  27. Accelerating Masked Image Generation by Learning Controlled Latent Dynamics

    Feb 27, 2026Kaiwen Zhu, Quansheng Zeng, Yuandong Pu +9Latent Dynamics ModelingT2I Generation

    Shanghai Jiao Tong University Shanghai, China · Shanghai AI Laboratory Shanghai, China · Shanghai Innovation Institute Shanghai, China +6

  28. ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks

    Feb 26, 2026Haohui Jia, Zheng Chen, Lingwei Zhu +6Latent Dynamics ModelingElectroencephalography

    Information Science and Techinology, Hokkaido University, Japan · SANKEN, The University of Osaka, Japan · Great Bay University, China +1

  29. 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

  30. Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems

    Feb 26, 2026Mahindra Rautela, Alexander ScheinkerLatent Dynamics ModelingInverse Problems

    Los Alamos National Laboratory, Los Alamos, NM, US