Dynamical Systems

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44 papers in the last four weeks, up 159% on the four weeks before. 0.4% of all new papers.

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Latest papers 319

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PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics
May 18, 2026Xueyu Luan, Chenwei ShiDynamical Systems Reinforcement Learning
School of Electronic and Information Engineering Tongji University Shanghai 200092, China · Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai 200092, China
A Unified Framework for Structured Flow Modeling: From Representation to Verification and Model Discovery
May 18, 2026Diego CasadeiDynamical Systems Hodge Decomposition
Fly High Engineering AG, Badenerstrasse 13, 5200 Brugg AG, Switzerland
Bimodal Synchronization Performance: Why Noise and Sparse Connectivity Can Improve Collective Timing
May 17, 2026Till Aust, Tianfu Zhang, Andreagiovanni Reina +1Dynamical Systems Multi-Agent Coordination
Department of Computer and Information Science, University of Konstanz, Konstanz, Germany · Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany · Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
Prediction Is Not Physics: Learning and Evaluating Conserved Quantities in Neural Simulators
May 16, 2026Andrew Bukowski, Aditya Kothari, Simba Shi +1Neural Surrogate Modeling Dynamical Systems
Yale University
Sharp Spectral Thresholds for Logit Fixed Points
May 15, 2026Tongxi WangDynamical Systems Lyapunov Stability
Southeast University Nanjing, China
Don't Stop Me Yet: Sampling Loss Minima via Dissipative Riemannian Mechanics
May 14, 2026Albert Kjøller Jacobsen, Leo Uhre Jakobsen, Johanna Marie Gegenfurtner +1Bayesian Neural Networks Dynamical Systems
Section for Cognitive Systems, Technical University of Denmark · Center for Quantum Information Physics, New York University
Time-Varying Deep State Space Models for Sequences with Switching Dynamics
May 14, 2026Sanja Karilanova, Subhrakanti Dey, Ayça ÖzçelikkaleDynamical Systems Regime-Switching Dynamical Systems
Department of Electrical Engineering, Uppsala University, Sweden
Dynamics of the Transformer Residual Stream: Coupling Spectral Geometry to Network Topology
May 14, 2026Jesseba Fernando, Grigori GuitchountsDynamical Systems Transformer
Network Science Institute, Northeastern University · Flagship Pioneering
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.
Coupling-Informed Transport Maps for Bayesian Filtering in Nonlinear Dynamical Systems
May 13, 2026Dengfei Zeng, Lijian Jiang, Shuyu Sun +1Dynamical Systems Simulation-Based Inference
School of Mathematical Sciences, Tongji University, Shanghai, 200092 China · School of Mathematical Sciences, Tongji University, Shanghai, 200092 China, Key Laboratory of Intelligent Computing and Applications (Ministry of Education), Tongji University, Shanghai, 200092 China
Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes
May 13, 2026Julien Martinelli, Maksim Sinelnikov, Harri Lähdesmäki +2Dynamical Systems Bayesian Inference
Aalto University · Inria SISTM team · Univ. Bordeaux, INSERM BPH, U1219, Inria SISTM team, VRI, France
Identifying the nonlinear string dynamics with port-Hamiltonian neural networks
May 12, 2026Maximino Linares, Guillaume Doras, Thomas HélieDynamical Systems Nonlinear System Identification
1IRCAM, 1, place Igor-Stravinsky, 75004, Paris, France · 2Sorbonne Université, 4, place Jussieu, 75005, Paris, France · 3CNRS, 3 rue Michel-Ange, 75016, Paris, France
BEHAVE: Real-Time Modeling of Human Systems as Observable Complex Dynamical Systems and Operational Objects for Physical AI
May 12, 2026Helene MalyutinaDynamical Systems Pedestrian Behavior Modeling
Independent Researcher, Collective Dynamics Lab
In-context learning to predict critical transitions in dynamical systems
May 12, 2026Yunus Sevinchan, Juan Nathaniel, Kai Ueltzhöffer +8Dynamical Systems In-Context Learning
kausable Heidelberg, Germany · Columbia University New York, USA · University of Edinburgh Scotland, UK
On the Importance of Multistability for Horizon Generalization in Reinforcement Learning
May 12, 2026Asad Bakija, Florent De Geeter, Julien Brandoit +2Dynamical Systems Reinforcement Learning
Dept. of Electrical Engineering and Computer Science · University of Liège · Liège, Belgium
Limits of Learning Linear Dynamics from Experiments
May 12, 2026Aybüke Ulusarslan, Niki Kilbertus, Nora SchneiderDynamical Systems Parameter Identifiability
1Technical University of Munich · 2Helmholtz Munich · 3Munich Center for Machine Learning (MCML)
Attractor-Vascular Coupling Theory: Formal Grounding and Empirical Validation for AAMI-Standard Cuffless Blood Pressure Estimation from Smartphone Photoplethysmography
May 11, 2026Timothy Oladunni, Farouk Ganiyu AdewumiDynamical Systems Photoplethysmography
Department of Computer Science, Morgan State University, Baltimore, MD 21251 USA
A general classification of the replication dynamics with a unique fixed point in the interior of simplex $S_N$
May 11, 2026Hongju Daisy Chen, Bin Yi, Zhanshan Sam MaDynamical Systems Game Theory
School of Mathematics and Statistics, Guilin University of Technology, Guilin, Guangxi, China · Guangxi Colleges and Universities Key Laboratory of Applied Statistics, Guilin, Guangxi, China · Computational Biology and Medical Ecology Lab Biostatistics and Image Genetics Lab Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China +2
Mixing Times of Glauber Dynamics on Masked Language Models
May 11, 2026Suvadip Sana, Sami Wolf, Neer Mehta +4Dynamical Systems Markov Chain Monte Carlo
Department of Statistics and Data Science Cornell University · Cornell University · Department Cornell of University Mathematics
Quantitative Local Convergence of Mean-Field Stein Variational Gradient Flow
May 10, 2026Lénaïc Chizat, Maria Colombo, Roberto Colombo +1Dynamical Systems Optimization Convergence Analysis
Nonlinear GENERIC Informed Neural Networks (N-GINNs): learning GENERIC dynamics with non-quadratic dissipation potentials
May 9, 2026Vojtěch Votruba, Zequn He, Weilun Qiu +2Dynamical Systems Nonlinear System Identification
Mathematical Institute, Faculty of Mathematics and Physics, Charles University, Prague, 18675, Czech Republic · Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, 19104, PA, United States of America
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
A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots
May 8, 2026Vincent Guan, Lazar Atanackovic, Kirill NeklyudovDynamical Systems
University of British Columbia · Mila - Quebec AI Institute · University of Alberta +4
The Power of Second Order Methods for Sequence Preconditioning
May 8, 2026Annie Marsden, Elad HazanDynamical Systems Sequence Modeling
Google DeepMind · Princeton University
Broken-symmetry shape discrimination on a driven Duffing ring
May 8, 2026Kaspar Anton SchindlerDynamical Systems Symmetry Breaking
Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
Bifurcation Models: Learning Set-Valued Solution Maps with Weight-Tied Dynamics
May 8, 2026Caleb Jore, Jialin LiuDynamical Systems
A Behavioral Framework for Data-Driven Modeling of Nonlinear Systems in Vector-Valued Reproducing Kernel Hilbert Spaces
May 8, 2026Boya Hou, Maxim RaginskyDynamical Systems Nonlinear System Identification
Carl R. Woese Institute for Genomic Biology and the Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, IL, 61801 USA · Department of Electrical and Computer Engineering and the Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, IL, 61801 USA
Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation
May 7, 2026Aditya Sai Ellendula, Yi Wang, Chandrajit BajajCollision Avoidance Dynamical Systems
Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA · Oden Institute, University of Texas at Austin, Austin, TX 78712, USA
When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
May 7, 2026Yi Wang, Chandrajit BajajDynamical Systems Learning to Optimize
1Oden Institute · Department of Computer Science
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

May 18, 2026, cs.LGJ/K move · Enter open · S save

PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics

Xueyu Luan, Chenwei Shi

School of Electronic and Information Engineering Tongji University Shanghai 200092, China · Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai 200092, China

Dynamical Systems Reinforcement Learning Physics-Informed ML Latent World Models Port-Hamiltonian Systems Hamiltonian Dynamics+1

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World models built on recurrent state space architectures enable efficient latent imagination, yet remain physically unstructured, producing dynamics that violate conservation and dissipative principles. We introduce a unified Port-Hamiltonian framework that remedies this through three synergistic mechanisms. First, we embed implicit physical priors into recurrent transitions by modeling projected latent evolution as action controlled energy routing governed by flow and dissipation, biasing the projected PH phase space toward a more compact and physically structured representation. Second, we develop a kinematics aware energy world model that estimates the Hamiltonian and power balance from proprioceptive observations, providing an explicit physical signal for thermodynamic reasoning. Third, leveraging these energy gradients, we establish an energy guided Actor-Critic that uses Lagrangian multipliers to regularize policy optimization toward lower energy and smoother control. Across visual control benchmarks, this paradigm not only attains superior asymptotic returns but also elevates internal simulator fidelity by establishing a tighter, lower variance alignment between imagined and real rewards, all while reducing latent phase space volume by 4.18-8.41%, energy consumption by up to 7.80%, and mean squared jerk by up to 9.38%.

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Latest papers 319