Imagen de Portada

Learning Passive Continuous-Time Dynamics with Multistep Port-Hamiltonian Gaussian Processes

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Leung, Chi Ho, Paré, Philip E.
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Systems and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Port-Transversal Barriers: Graph-Theoretic Safety for Port-Hamiltonian Systems
por: Leung, Chi Ho, et al.
Publicado: (2026)

Bayesian Safety Guarantees for Port-Hamiltonian Systems with Learned Energy Functions
por: Leung, Chi Ho, et al.
Publicado: (2025)

Spectral Flow Learning Theory: Finite-Sample Guarantees for Vector-Field Identification
por: Leung, Chi Ho, et al.
Publicado: (2025)

Modeling Epidemic Spread: A Gaussian Process Regression Approach
por: She, Baike, et al.
Publicado: (2023)

Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
por: Ewering, Jan-Hendrik, et al.
Publicado: (2025)

Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
por: van Otterdijk, G. J. E., et al.
Publicado: (2024)

Online Identification of Time-Varying Systems Using Excitation Sets and Change Point Detection
por: Leung, Chi Ho, et al.
Publicado: (2024)

Collaborative Safe Formation Control for Coupled Multi-Agent Systems
por: Butler, Brooks A., et al.
Publicado: (2023)

Structure- and Stability-Preserving Learning of Port-Hamiltonian Systems
por: Nguyen, Binh, et al.
Publicado: (2026)

Collaborative Safety-Critical Formation Control with Obstacle Avoidance
por: Butler, Brooks A., et al.
Publicado: (2024)

Multistep Inverse Is Not All You Need
por: Levine, Alexander, et al.
Publicado: (2024)

A Physics-Informed Neural Networks-Based Model Predictive Control Framework for $SIR$ Epidemics
por: Zhong, Aiping, et al.
Publicado: (2025)

Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
por: Kamboj, Ankur, et al.
Publicado: (2026)

Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models
por: Tan, Kaiyuan, et al.
Publicado: (2024)

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach
por: Zakwan, Muhammad, et al.
Publicado: (2024)

Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks
por: Heilig, Simon, et al.
Publicado: (2024)

Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Version
por: Yang, Zewen, et al.
Publicado: (2025)

Asynchronous Distributed Gaussian Process Regression for Online Learning and Dynamical Systems: Complementary Document
por: Yang, Zewen, et al.
Publicado: (2024)

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks
por: Neary, Cyrus, et al.
Publicado: (2024)

Hybrid Energy-Based Models for Physical AI: Provably Stable Identification of Port-Hamiltonian Dynamics
por: Betteti, Simone, et al.
Publicado: (2026)

Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics
por: Prajapat, Manish, et al.
Publicado: (2025)

Peak Infection Time for a Networked SIR Epidemic with Opinion Dynamics
por: She, Baike, et al.
Publicado: (2021)

Learning Hamiltonian Dynamics with Bayesian Data Assimilation
por: Kim, Taehyeun, et al.
Publicado: (2025)

Learning of Hamiltonian Dynamics with Reproducing Kernel Hilbert Spaces
por: Smith, Torbjørn, et al.
Publicado: (2023)

Efficient Data-Driven MPC for Demand Response of Commercial Buildings
por: Paré, Marie-Christine, et al.
Publicado: (2024)

Koopman-Equivariant Gaussian Processes
por: Bevanda, Petar, et al.
Publicado: (2025)

Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models
por: Zhang, Yu, et al.
Publicado: (2024)

PHAST: Port-Hamiltonian Architecture for Structured Temporal Dynamics Forecasting
por: Bhardwaj, Shubham, et al.
Publicado: (2026)

Learning Stable and Passive Neural Differential Equations
por: Cheng, Jing, et al.
Publicado: (2024)

Operator Models for Continuous-Time Offline Reinforcement Learning
por: Hoischen, Nicolas, et al.
Publicado: (2025)

Polynomial Chaos Expanded Gaussian Process
por: Polke, Dominik, et al.
Publicado: (2024)

Learning Hamiltonian Dynamics with Reproducing Kernel Hilbert Spaces and Random Features
por: Smith, Torbjørn, et al.
Publicado: (2024)

Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
por: Zhong, Yaofeng Desmond, et al.
Publicado: (2019)

Multistep Criticality Search and Power Shaping in Microreactors with Reinforcement Learning
por: Radaideh, Majdi I., et al.
Publicado: (2024)

Singular Port-Hamiltonian Systems Beyond Passivity
por: Sandberg, Henrik, et al.
Publicado: (2026)

Frequency-Separable Hamiltonian Neural Network for Multi-Timescale Dynamics
por: Li, Yaojun, et al.
Publicado: (2026)

Quasi-Periodic Gaussian Process Predictive Iterative Learning Control
por: Nigam, Unnati, et al.
Publicado: (2026)

Stability Enhanced Gaussian Process Variational Autoencoders
por: Richardson, Carl R., et al.
Publicado: (2026)

Recursive Gaussian Process State Space Model
por: Zheng, Tengjie, et al.
Publicado: (2024)

Impact of Computation in Integral Reinforcement Learning for Continuous-Time Control
por: Cao, Wenhan, et al.
Publicado: (2024)