Latent Dynamics Modeling

Follow this topic Ask about this topic

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

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

From Observed Viability to Internal Predictive Approximation: A Single-Subject Latent-Space Analysis of Gait Dynamics Under Occlusal Constraint
May 15, 2026Jacques Raynal, Pierre Slangen, Elsa Raynal +1Latent Dynamics Modeling Gait Analysis
Laboratory of Bioengineering and Nanosciences (LBN), University of Montpellier, France · 2EuroMov Digital Health in Motion, University of Montpellier, IMT Mines Alès, Alès, France · 3Certified Sophrologist and Dental Assistant, Sensorimotor Practice, Montpellier, France +1
Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics
May 14, 2026Yunfei Luo, Xi Chen, Yuliang Chen +8Multivariate Time Series Forecasting World Model Learning
University of California San Diego · Dartmouth College · Amazon Web Services +3
Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation
May 14, 2026Rishabh Dev Yadav, Samaksh Ujjawal, Shivansh Pratap Singh +4Aerial Robotics Online System Identification
Department of Computer Science, The University of Manchaster, U.K. · Department of Cognitive Robotics, Delft University of Technology, Netherlands · International Institute of Information Technology Hyderabad. +1
DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes
May 14, 2026Manrui Jiang, Jingru Huang, Yong Chen +1Multivariate Time Series Forecasting Reinforcement Learning
Department of Industrial Engineering, Tsinghua University, Beijing 100084, China · Department of Industrial and Systems Engineering, University of Iowa, Iowa City, IA 52242, USA.
Wavelet-Based Observables for Koopman Analysis: An Extended Dynamic Mode Decomposition Framework
May 14, 2026Cankat Tilki, Serkan GugercinDynamical Systems Koopman Operator Learning
Department of Mathematics, Virginia Tech, Blacksburg, VA - 24061.
Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform
May 13, 2026Feisal Alaswad, Batoul Aljaddouh, Maher Alrahhal +2Latent Dynamics Modeling Long-Horizon Agent Evaluation
Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur 603203, Tamil Nadu, India · Bio-Sensing and Bio-Sensors Group Smart Automation and Communication Technologies Research Institute of Sciences and Engineering University of Sharjah, UAE · Department of Computer Engineering College of Computing and Informatics University of Sharjah, UAE
Support-Safe Variational Hybrid Filtering for Contact-Mode and Sparse-Law Recovery
May 12, 2026Marios Papamichalis, Regina RuaneLatent Dynamics Modeling System Identification
Human Nature Lab, Yale University, New Haven, CT 06511 · Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, 3733 Spruce Street, Philadelphia, PA 19104-6340
Stable Long-Horizon PDE Forecasting via Latent Structured Spectral Propagators
May 11, 2026Xiaoxiao Lu, Ye Yuan, Jiahao ShiLatent Dynamics Modeling PDE Surrogate Modeling
School of AIA, Huazhong University of Science and Technology
Event Fields: Learning Latent Event Structure for Waveform Foundation Models
May 9, 2026Li Na, Yuanyun Zhang, Shi LiIrregular Time-Series Modeling Latent Dynamics Modeling
University of Science and Technology of China · Columbia University
FLUX: Geometry-Aware Longitudinal Flow Matching with Mixture of Experts
May 9, 2026Josue Ortega Caro, Yongxu Zhang, Hannah M Batchelor +3Dynamical Systems Flow Matching
Yale University
End-to-End Identifiable and Consistent Recurrent Switching Dynamical Systems
May 7, 2026Carles Balsells-Rodas, Zhengrui Xiang, Xavier Sumba +1Dynamical Systems Latent Variable Models
Imperial College London
Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations
May 7, 2026Jan-Hendrik Ewering, Kathrin Flaßkamp, Niklas Wahlström +2Latent Dynamics Modeling Physics-Informed ML
Leibniz Universität Hannover · Saarland University · Uppsala University
HaM-World: Soft-Hamiltonian World Models with Selective Memory for Planning
May 7, 2026Haoyun Tang, Haodong Cui, Keyao Xu +2World Model Learning Latent Dynamics Modeling
1Xi’an Jiaotong University · 2Huazhong University of Science and Technology · 3Nankai University +1
Online Generalised Predictive Coding
May 4, 2026Mehran H. Z. Bazargani, Szymon Urbas, Adeel Razi +2Parameter Estimation Latent Dynamics Modeling
Insight Research Ireland Centre, UCD, Dublin, Ireland. · UCD School of Mathematics and Statistics, UCD, Belfield, Dublin, Ireland. · Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia. +5
Closed-Loop CO2 Storage Control With History-Based Reinforcement Learning and Latent Model-Based Adaptation
May 4, 2026Sofianos Panagiotis Fotias, Vassilis GaganisRL Control Latent Dynamics Modeling
School of Mining and Metallurgical Engineering, National Technical University of Athens, Athens 157 73, Greece · Institute of Geoenergy, Foundation for Research and Technology, Chania, 73100, Greece
A Universal Space of Brain Dynamics for Unveiling Cognitive Transitions and Individual Differences
May 1, 2026Ronghua Zheng, Chengyuan Qian, Weiyang DingLatent Dynamics Modeling Functional Magnetic Resonance Imaging
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. · Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.
Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
May 1, 2026Sen Cui, Jingheng MaLatent Dynamics Modeling World Models
Tsinghua University
RopeDreamer: A Kinematic Recurrent State Space Model for Dynamics of Flexible Deformable Linear Objects
Apr 30, 2026Tim Missal, Lucas Domingues, Berk Guler +3Latent Dynamics Modeling Robotic Manipulation
Technical University of Darmstadt · School of Electrical and Computer Engineering, Universidade Estadual de Campinas (UNICAMP), Brazil · Instituto de Pesquisas Eldorado, Brazil +5
Biased Dreams: Limitations to Epistemic Uncertainty Quantification in Latent Space Models
Apr 28, 2026Julia Berger, Bernd Frauenknecht, Sebastian Trimpe +1Uncertainty Quantification Latent Dynamics Modeling
1RWTH Aachen University
Integrative neurocybernetic modeling in the era of large-scale neuroscience
Apr 26, 2026Il Memming Park, Gonzalo G. de Polavieja, Ayesha Vermani +12Computational Cognitive Modeling Latent Dynamics Modeling
Champalimaud Centre for the Unknown, Champalimaud Foundation, Lisbon, Portugal · RyvivyR Inc, NY, USA · Harvard University, Cambridge, MA, USA +3
Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs
Apr 24, 2026Jose Geraldo Fernandes, Luiz Facury, Pedro Robles Dutenhefner +1Predictive Representation Learning Healthcare
Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte, Brazil
Early Detection of Latent Microstructure Regimes in Limit Order Books
Apr 22, 2026Prakul Sunil Hiremath, Vruksha Arun HiremathQuantitative Finance Latent Dynamics Modeling
Department of Computer Science and Engineering Visvesvaraya Technological University, Belagavi · Department of Business Administration K.L.E. Society’s College of Business Administration
AC-SINDy: Compositional Sparse Identification of Nonlinear Dynamics
Apr 20, 2026Peter RacioppoLatent Dynamics Modeling Sparse Identification of Nonlinear Dynamics
Independent Researcher Los Angeles, CA 90731
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
Geometric regularization of autoencoders via observed stochastic dynamics
Apr 17, 2026Sean Hill, Felix X. -F. YeLatent Dynamics Modeling Autoencoders
Department of Mathematics and Statistics, University at Albany, SUNY
Interpretable Relational Inference with LLM-Guided Symbolic Dynamics Modeling
Apr 14, 2026Xiaoxiao Liang, Juyuan Zhang, Liming Pan +1Latent Dynamics Modeling Symbolic Regression
School of Cyber Science and Technology, University of Science and Technology of China, Hefei, Anhui, China.
Stability Enhanced Gaussian Process Variational Autoencoders
Apr 10, 2026Carl R. Richardson, Jichen Zhang, Ethan King +1Latent Dynamics Modeling Variational Autoencoders
Johns Hopkins University, Baltimore, Maryland, 21218, USA
A deep learning framework for jointly solving transient Fokker-Planck equations with arbitrary parameters and initial distributions
Apr 7, 2026Xiaolong Wang, Jing Feng, Qi Liu +3PDE Solving Neural PDE Solvers
School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, 710119, China · School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, 710129, China · MOE Key Laboratory for Complexity Science in Aerospace, Northwestern Polytechnical University, Xi’an, 710072, China +2
A Robust SINDy Autoencoder for Noisy Dynamical System Identification
Apr 6, 2026Kairui DingNonlinear System Identification System Identification
Department of Mathematics, Columbia University
On Data-Driven Koopman Representations of Nonlinear Delay Differential Equations
Apr 3, 2026Santosh Mohan Rajkumar, Dibyasri Barman, Kumar Vikram Singh +1Extended Dynamic Mode Decomposition Dynamical Systems
Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH, USA. · Department of Chemical, Paper, and Biomedical Engineering at Miami University, Oxford, OH, USA. · Department of Mechanical and Manufacturing Engineering at Miami University, Oxford, OH, USA.

May 15, 2026J/K move · Enter open · S save

From Observed Viability to Internal Predictive Approximation: A Single-Subject Latent-Space Analysis of Gait Dynamics Under Occlusal Constraint

Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit

Laboratory of Bioengineering and Nanosciences (LBN), University of Montpellier, France · 2EuroMov Digital Health in Motion, University of Montpellier, IMT Mines Alès, Alès, France · 3Certified Sophrologist and Dental Assistant, Sensorimotor Practice, Montpellier, France+1

Latent Dynamics Modeling Gait Analysis Parkinson's Disease

Open paper AskPDF arXiv

Understanding adaptive biomechanical systems requires distinguishing observable performance, static multivariate representation, longitudinal displacement, and internal approximation of observed change. This study introduces Level 5, which examines whether the M1-M2 transformation observed in a single-subject gait dataset can be approximated within a selected PCA representation. Gait was recorded with instrumented insoles in a participant with Parkinson's disease under six occlusal observational probes during two sessions eleven weeks apart. A simplified feed-forward neural network was trained to approximate M2 PC1-PC2 coordinates from M1 coordinates, occlusal-probe descriptors, and the longitudinal-transition indicator. In the core analysis aligned with Level 4, the model preserved the Euclidean centroid-displacement hierarchy dOC3 < dONL < dOC2.5. In the extended six-probe analysis, it preserved the broad structure of the exploratory ordering. Held-out M2 and leave-condition-out analyses provided internal tests beyond the full-dataset fit, while a within-session analysis described probe positions relative to ONL. The term predictive is used only in a restricted methodological sense. The model does not provide prospective clinical prediction, patient-level forecasting, or generalization to unseen individuals. Occlusal conditions are treated as observational probes applied during measurement, not as continuous causal drivers of longitudinal evolution. The findings are exploratory, retrospective, representation dependent, and non causal. They do not establish causal occlusal effects, validated viability thresholds, therapeutic superiority, distinct physiological states, or generalizable predictive validity.