Port-Hamiltonian Systems

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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Port-Hamiltonian Neural Networks for Systems with Multiple Asymptotically Stable Equilibria
Oct 1, 2026Simon Heilig, Jens Püttschneider, Mohammad Itani +2Dynamical Systems Lyapunov Stability
Faculty of Computer Science, Ruhr University Bochum, Germany · Institute of Control Systems, Hamburg University of Technology, Germany
Port-Hamiltonian Latent Deliberation: Mitigating the Deliberation Drift Cliff in Test-Time Compute Scaling
Sep 29, 2026Zeyu JiaHodge Decomposition Inference-Time Scaling
School of Biomedical Engineering and Technology, Tianjin Medical University, Medical School, Tianjin University
Port-Hamiltonian Koopman Operator Synthesis for Mechanical Systems
Sep 15, 2026Rajpal Singh, Aditya Singh, Jishnu KeshavanRobot Dynamics Identification Koopman Operator Learning
Department of Mechanical Engineering, Indian Institute of Science, Bangalore, Karnataka 560012, India
Quantum Port-Hamiltonian Neural Networks: Learning Conservative and Dissipative Dynamics via Measurement-Induced Nonlinearity
Jul 14, 2026Dibakar SigdelHamiltonian Neural Networks Port-Hamiltonian Systems
1Mindverse Computing LLC, Lynnwood, WA 98087
Learning the Brain's Dynamics as a Port-Hamiltonian System
Jul 11, 2026Dibakar SigdelDynamical Systems Brain-Computer Interfaces
Mindverse Computing LLC, Lynnwood, WA 98087 · 1Mindverse Computing LLC, Lynnwood, WA 98087
Learning-Based Modeling of Soft Robots via Cosserat Rod Theory
Jun 18, 2026Mohammad Ali, Nithin Senthur Kumar, Eric J. Barth +1Dynamical Systems Port-Hamiltonian Systems
Department of Computer Science, Vanderbilt University, Nashville, TN 37235, USA · Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37235, USA
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
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
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
Mesh Field Theory: Port-Hamiltonian Formulation of Mesh-Based Physics
May 1, 2026Satoshi Noguchi, Yoshinobu KawaharaDynamical Systems Scientific ML
Center for Mathematical Science and Advanced Technology, JAMSTEC · Center for Advanced Intelligence Project, RIKEN · Graduate School of Information Science and Technology, The University of Osaka.
Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
Apr 28, 2026Ankur Kamboj, Biswadip Dey, Vaibhav SrivastavaDynamical Systems Feedback Control
Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824 · Meta Reality Labs, Redmond, WA 98052
Early-Terminable Energy-Safe Iterative Coupling for Parallel Simulation of Partitioned Port-Hamiltonian Systems
Mar 17, 2026Qi Wei, Jianfeng Tao, Hongyu Nie +1Dynamical Systems Port-Hamiltonian Systems
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Oct 1, 2026, cs.LGJ/K move · Enter open · S save

Port-Hamiltonian Neural Networks for Systems with Multiple Asymptotically Stable Equilibria

Simon Heilig, Jens Püttschneider, Mohammad Itani, Asja Fischer+1

Faculty of Computer Science, Ruhr University Bochum, Germany · Institute of Control Systems, Hamburg University of Technology, Germany

Dynamical Systems Lyapunov Stability Port-Hamiltonian Systems

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Stable port-Hamiltonian neural networks certify asymptotic stability by construction. Yet, their Hamiltonian is a global Lyapunov function with a single global minimum, so they can represent only dynamic systems with one attractor. We demonstrate that this excludes even simple systems with energy landscapes forming a double well, and we overcome the restriction by parametrising the Hamiltonian as a product of Bregman divergences generated by one input-convex network. We prove that the resulting model is locally Lyapunov stable, that the coexistence of stable equilibria forces additional non-asymptotically-stable equilibria to exist, that all equilibria lie in a bounded region, and under a hyperbolicity assumption that almost-everywhere stability holds. On three systems our approach is able to recover the energy surface characteristics and improve the convergence speed by 1.8

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