Port-Hamiltonian Systems

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Port-Hamiltonian systems describe how energy is stored, dissipated, and exchanged with external inputs, making them useful for modeling and controlling mechanical, electrical, and other physical systems. Researchers derive these models using geometric mechanics and learn them with neural networks that enforce skew-symmetric interconnections and positive-semidefinite dissipation; structure-preserving integrators help retain energy and passivity properties in simulation. Current applications include oscillators, robots, soft bodies, latent dynamics from images, and physics-informed world models for control. A key tension is that structural guarantees can improve stability and extrapolation, but flexible models may be hard to identify uniquely without sufficient physical anchors, and preserving multiple stable equilibria can complicate stability constraints.

Latest papers

23 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Energy-Based Fault Detection and Isolation Framework for Flat Port-Hamiltonian Mechanical Systems
Oct 24, 2026Johnny Garcia, Nicolas Salguero, Naveed Iqbal +2Fault Detection Fault Diagnosis
Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia · Electrical Engineering Department and the Interdisciplinary Research Center for Communication Systems and Sensing, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia · Electrical Engineering Department and the Interdisciplinary Research Center for Intelligent Manufacturing and Robotics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia +1
Structure-Aware Spatial Kernels for Gaussian Process Distributed Port-Hamiltonian Systems
Oct 15, 2026Tianyi Yang, Thomas BeckersKernel Methods Gaussian Processes
Department of Computer Science, Vanderbilt University, Nashville, TN 37212, USA · Institute for Measurement, Control and Microtechnology, Ulm University, 89081 Ulm, Germany
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 BajajRisk-Sensitive RL 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
Structure- and Stability-Preserving Learning of Port-Hamiltonian Systems
Apr 14, 2026Binh Nguyen, Nam T. Nguyen, Truong X. NghiemPort-Hamiltonian Systems Hamiltonian Neural Networks
Department of Electrical and Computer Engineering, College of Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA
PH-KAN: Port-Hamiltonian Kolmogorov-Arnold Network
Apr 7, 2026Achraf El Messaoudi, Karim Cherifi, Yann Le Gorrec +1Nonlinear System Identification Port-Hamiltonian Systems
UMLP, ENSMM, FEMTO-ST · Université Marie et Louis Pasteur, SUPMICROTECH, CNRS, institut FEMTO-ST, F-25000 Besançon, France
Hybrid Energy-Based Models for Physical AI: Provably Stable Identification of Port-Hamiltonian Dynamics
Mar 31, 2026Simone Betteti, Luca LaurentiEnergy-Based Models Dynamical Systems
RIAS Lab, The Italian Institute of Artificial Intelligence for Industry, Turin, 10129, IT · Delft Center for Systems and Control, TU Delft, Delft, 2600 AA, The Netherlands
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
Stochastic Port-Hamiltonian Neural Networks: Universal Approximation with Passivity Guarantees
Mar 10, 2026Luca Di Persio, Matthias Ehrhardt, Youness OutalebPort-Hamiltonian Systems Hamiltonian Neural Networks
Department of Computer Science - College of Mathematics University of Verona, Strada le Grazie 15 - 37134 Verona, Italy · University of Wuppertal, Chair of Applied and Computational Mathematics, Gaußstrasse 20, 42119 Wuppertal, Germany · Doctoral School in Mathematics, University of Trento Via Sommarive, 14 - 38123 Povo, Italy
The Port-Hamiltonian Structure of Vehicle Manipulator Systems
Feb 25, 2026Ramy RashadRobotics Port-Hamiltonian Systems
Control and Instrumentation Engineering department and Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran, 34464, Saudi Arabia
PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting
Feb 20, 2026Shubham Bhardwaj, Chandrajit BajajTime Series Forecasting Port-Hamiltonian Systems
Department of Computer Science, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA
Controlled oscillation modeling using port-Hamiltonian neural networks
Feb 17, 2026Maximino Linares, Guillaume Doras, Thomas HéliePort-Hamiltonian Systems Hamiltonian Dynamics
IRCAM, 1, place Igor-Stravinsky, 75004, Paris, France
Discovering Phase Space Structure in Learned Hamiltonian Systems
Sep 26, 2025Yulong Yang, Jiayin Liu, Vineet Bansal +1Latent Dynamics Modeling Hamiltonian Dynamics
Princeton University, USA · Tsinghua University, China

Oct 24, 2026J/K move · Enter open · S save

Energy-Based Fault Detection and Isolation Framework for Flat Port-Hamiltonian Mechanical Systems

Johnny Garcia, Nicolas Salguero, Naveed Iqbal, Brahim Brahmi+1

Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia · Electrical Engineering Department and the Interdisciplinary Research Center for Communication Systems and Sensing, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia · Electrical Engineering Department and the Interdisciplinary Research Center for Intelligent Manufacturing and Robotics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia+1

Fault Detection Fault Diagnosis Port-Hamiltonian Systems

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Fault diagnosis during regulated motion requires informative signals even when feedback attenuates tracking deviations. This paper develops an energy-based diagnostic framework that constructs mechanical port-power residuals without a dedicated dynamic observer. Spectral Hamiltonian Algebraic Power Evaluation (SHAPE) combines port-Hamiltonian variables and differential flatness to generate nominal power references from planned motion for fully actuated mechanical systems with collocated actuation. Subtracting these references from commanded effort multiplied by measured flow yields residuals whose final evaluation requires neither recursive observer updates nor measured-state dynamics calculations. Continuous wavelet transform (CWT) scalograms and a shared-encoder convolutional neural network are used to distinguish nominal operation, actuator loss of effectiveness, anomalous friction, and vibration from complete trajectories. Evaluation uses a simulated six degree of freedom Kinova manipulator under model predictive control and fixed plant-model mismatch, with single faults at a specified joint. Across five training seeds and 3000 held-out test runs, SHAPE-CWT achieves a mean accuracy of 84.69 percent, versus 81.67 percent for torque residuals and 97.17 percent for the generalized momentum observer under common CWT processing. The respective sample standard deviations are 0.70, 0.70, and 0.37 percentage points. These results demonstrate useful fault-type discrimination from algebraic power residuals, while severity-excitation profiles and offline velocity perturbations reveal limits to their diagnostic capability.