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

CardsList

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

  1. Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics

    Feb 23, 2026Yue Pan, Xingyao Wang, Hanyue Zhang +6Remote Patient MonitoringClinical Speech Processing

    School of Information Science and Technology, Southeast University, Nanjing, China · Department of Cardiology, Chongqing Hospital of Jiangsu Province Hospital, Chongqing, China · Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China +3

  2. LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification

    Feb 20, 2026Yicheng Feng, Hairong Chen, Ziyu Jia +2Alzheimer's Disease ClassificationLatent Dynamics Modeling

    Section of Health Data Science & AI, Department of Public Health, University of Copenhagen, Copenhagen, Denmark · Institute of Information Science, Beijing Jiaotong University, Beijing, China · College of Computing and Data Science, Nanyang Technological University, Singapore +3

  3. Position: A Dynamical Systems Perspective is Needed to Advance Time Series Modeling

    Feb 18, 2026Daniel Durstewitz, Christoph Jürgen Hemmer, Florian Hess +2Time Series ModelingTime Series Forecasting

    Dept. of Theoretical Neuroscience, Central Institute of Mental Health, Mannheim, Germany · Faculty of Physics & Astronomy, Heidelberg Univ., Heidelberg, Germany · Interdisciplinary Center for Scientific Computing, Heidelberg, Germany

  4. Factored Latent Action World Models

    Feb 18, 2026Zizhao Wang, Chang Shi, Jiaheng Hu +4Latent World ModelsLatent Action Learning

    University of Texas at Austin · Sony AI

  5. Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks

    Feb 16, 2026Ramón Nartallo-Kaluarachchi, Renaud Lambiotte, Alain GorielyRecurrent Neural NetworksDynamical Systems

    Mathematical Institute, University of Oxford, Woodstock Road, Oxford, OX2 6GG, United Kingdom · Centre for Eudaimonia and Human Flourishing, University of Oxford, 7 Stoke Pl, Oxford, OX3 9BX, United Kingdom · Complexity Science Hub, Metternichgasse 8, Vienna, 1030, Austria

  6. Textual Planning with Explicit Latent Transitions

    Feb 4, 2026Eliezer Shlomi, Ido Levy, Eilam Shapira +6Latent Dynamics ModelingLLM Planning

    Technion – Israel Institute of Technology, Haifa, Israel · IBM, Haifa, Israel.

  7. Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling

    Dec 12, 2025Marco Baiesi, Alberto RossoLatent Variable ModelsLatent Dynamics Modeling

    Department of Physics and Astronomy, University of Padova, Via Marzolo 8, I-35131 Padova, Italy · INFN, Sezione di Padova, Via Marzolo 8, I-35131 Padova, Italy · LPTMS, CNRS, Universit´e Paris-Saclay, 91405, Orsay, France

  8. Discovering Phase Space Structure in Learned Hamiltonian Systems

    Sep 26, 2025Yulong Yang, Jiayin Liu, Vineet Bansal +1Latent Dynamics ModelingHamiltonian Dynamics

    Princeton University, USA · Tsinghua University, China

  9. Interpretable time series analysis with Gumbel dynamics

    Sep 25, 2025Yiliu Wang, Timothy Doyeon Kim, Eric Shea-Brown +1Time Series ModelingLatent Dynamics Modeling

    Allen Institute · University of Washington

  10. Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling

    Aug 23, 2025Malinda Lu, Yue-Jane Liu, Matthew K. Nock +1Stochastic Differential EquationsLatent Dynamics Modeling

    Wellesley College · Harvard University

  11. Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics

    Jul 21, 2025Alex Guo, Michael D. GrahamReduced-Order ModelingChaotic Time-Series Prediction

    Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison WI 53706, USA

  12. Latent Evolution Operator (LEO): State-to-State Forecasting of Parametric PDEs

    May 14, 2025Khalid Rafiq, Wenjing Liao, Aditya G. NairPDE Surrogate ModelingPDE Operator Learning

    Department of Mechanical Engineering, University of Nevada, Reno, Reno, USA · School of Mathematics, Georgia Institute of Technology, Atlanta, USA

  13. Stochastic Optimal Control for Continuous-Time fMRI Representation Learning

    Feb 7, 2025Joonhyeong Park, Byoungwoo Park, Chang-Bae Bang +4Latent Dynamics ModelingSelf-Supervised Time Series Representation Learning

    KAIST · Yonsei University · EverEx +1

  14. Automated Discovery of Operable Dynamics from Videos

    Oct 14, 2024Kuang Huang, Dong Heon Cho, Boyuan ChenDynamical SystemsLatent Dynamics Modeling

    Department of Applied Physics and Applied Mathematics, Columbia University, 500 W. 120th Street, New York, 10027, NY, USA. · Department of Computer Science, Duke University, 308 Research Dr., Durham, 27705, NC, USA. · Department of Mechanical Engineering and Material Science, Duke University, 121 Hudson Hall, Durham, 27708, NC, USA. +1