Latent Dynamics Modeling

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35 papers in the last four weeks, up 483% on the four weeks before. 0.3% of all new papers.

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Autoregression-Free Neural Operators for Time-Dependent PDEs
May 25, 2026Jiaquan Zhang, Caiyan Qin, Haoyu Bian +7Latent Dynamics Modeling PDE Operator Learning
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China · School of Robotics and Advanced Manufacture, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China · School of Mathematical Sciences, Capital Normal University, Beijing 100048, China +3
UWM-JEPA: Predictive World Models That Imagine in Belief Space
May 25, 2026Santosh Kumar Radha, Oktay GoktasWorld Model Learning Latent Dynamics Modeling
AgentField AI, Toronto, Canada
Deep ZakaiJ: Structured Filtering for Jump-Diffusion Time Series Forecasting
May 23, 2026Yan Leng, Thibaut Mastrolia, Hao WangStochastic Differential Equations Latent Dynamics Modeling
University of Texas at Austin · University of California, Berkeley
Learning partially observed systems with neural Hamiltonian ordinary differential equations
May 22, 2026Sunniva Meltzer, Sølve Eidnes, Alexander Johannes StasikDynamical Systems Latent Dynamics Modeling
Department of Mathematics and Cybernetics, SINTEF Digital, Norway · Department of Physics, University of Oslo, Norway · Department of Data Science, Norwegian University of Life Science, Norway
Learning Individual Dynamics from Sparse Cross-Sectional Snapshots
May 22, 2026Christian Lagemann, Kai Lagemann, Steven L. Brunton +1Parameter Identifiability Latent Dynamics Modeling
Statistics and Machine Learning, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany · MediaTek Research, London, United Kingdom · Department of Mechanical Engineering & AI Institute in Dynamic Systems, University of Washington, Seattle, United States +1
Dreaming Smoothly and Sample Efficiently with Gradient Penalized Latent Dynamics
May 21, 2026Romil V. Sonigra, P. R. KumarLatent Dynamics Modeling Jacobian Regularization
Texas A&M University · Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843
EMMA: Extracting Multiple physical parameters from Multimodal Data
May 21, 2026Farhat Shaikh, Ayan Banerjee, Sandeep GuptaParameter Estimation Latent Dynamics Modeling
IMPACT Lab, School of Computing & Augmented Intelligence (SCAI) Arizona State University, Tempe, AZ
From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching
May 21, 2026Siyu Pu, Qingqing Long, Xiaohan Huang +7Latent Dynamics Modeling Conditional Flow Matching
Computer Network Information Center, Chinese Academy of Sciences · University of California, Los Angeles
ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data
May 21, 2026Jiangyuan Wang, Xuyong Chen, Junwei He +3Latent Dynamics Modeling Healthcare
Beijing KidneyTec Medical Technology Co., Ltd., Beijing, China
Latent Dynamics for Full Body Avatar Animation
May 20, 2026Shichong Peng, Chengxiang Yin, Fei Jiang +7Latent Dynamics Modeling Human Motion Generation
Simon Fraser University, Canada · Codec Avatars Lab, Meta, USA
Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport
May 20, 2026Mehmet Yigit Balik, Harri LähdesmäkiLatent Dynamics Modeling Conditional Generative Modeling
Department of Computer Science, Aalto University, Espoo, Finland.
Time-Dependent PDE-Constrained Optimization via Weak-Form Latent Dynamics
May 20, 2026April Tran, Terry Haut, David Bortz +1Latent Dynamics Modeling PDE 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
L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecasting
May 18, 2026Fan Zhang, Shijun Chen, Hua WangMultivariate Time Series Forecasting Latent Dynamics Modeling
Shandong Technology and Business University, Yantai, Shandong, China · Ludong University, Yantai, Shandong, China
Factorized Latent Dynamics for Video JEPA: An Empirical Study of Auxiliary Objectives
May 16, 2026Santosh PremiDisentangled Representation Learning Latent Dynamics Modeling
Identify Then Project: Contrastive Learning of Latent Dynamics from Partial Observations with Port-Hamiltonian Structure
May 15, 2026Peilun Li, Kaiyuan Tan, Daniel Moyer +1Contrastive Learning Latent Dynamics Modeling
Department of Computer Science Vanderbilt University Nashville, TN 37235
Hypothesis-driven construction of mesoscopic dynamics
May 15, 2026Zhuoyuan Li, Aiqing Zhu, Qianxiao LiLatent Dynamics Modeling Nonlinear System Identification
Institute for Functional Intelligent Materials, National University of Singapore · Department of Mathematics, National University of Singapore
Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making
May 15, 2026Fan Feng, Selena Ge, Minghao Fu +6Latent Action Learning Latent Dynamics Modeling
University of California San Diego · MBZUAI · Carnegie Mellon University +2
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, Sihao Sun +2Aerial 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

May 25, 2026, cs.LGJ/K move · Enter open · S save

Autoregression-Free Neural Operators for Time-Dependent PDEs

Jiaquan Zhang, Caiyan Qin, Haoyu Bian, Libin Cai+6

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China · School of Robotics and Advanced Manufacture, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China · School of Mathematical Sciences, Capital Normal University, Beijing 100048, China+3

Latent Dynamics Modeling PDE Operator Learning Spatiotemporal Forecasting

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Neural operators learn mappings from function-dependent inputs to solutions, providing an effective framework for solving partial differential equations (PDEs). For time-dependent PDEs, existing methods typically perform long-horizon prediction through autoregressive rollout directly in high-dimensional physical field spaces, where each predicted state is recursively fed back as the input for the next step. Although effective for short-term prediction, this autoregressive rollout and the lack of continuous-time modeling lead to progressive error accumulation over long-horizon rollouts. In this work, we propose Autoregression-Free Neural Operators (AFNO), which map the time evolution of PDEs into a latent space and model continuous-time vector fields within it. AFNO uses flow matching to learn the latent vector field, thereby enabling continuous evolution over extended horizons, avoiding autoregressive rollout and capturing dynamics under varying parameter configurations through explicit conditioning on physical parameters. Theoretical analysis and extensive experiments on six PDEs demonstrate that AFNO improves long-horizon prediction stability and consistently reduces rollout errors compared with the baselines.

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Latest papers 172