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Compositional Learning of Dynamical System Models Using Port-Hamiltonian
  Neural Networks

Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks

1 December 2022
Cyrus Neary
Ufuk Topcu
    PINN
    AI4CE
ArXivPDFHTML

Papers citing "Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks"

11 / 11 papers shown
Title
Stable Port-Hamiltonian Neural Networks
Stable Port-Hamiltonian Neural Networks
Fabian J. Roth
Dominik K. Klein
Maximilian Kannapinn
Jan Peters
Oliver Weeger
74
1
0
04 Feb 2025
Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks
Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks
Cyrus Neary
Nathan Tsao
Ufuk Topcu
77
1
0
15 Dec 2024
Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems
  across Domains
Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains
Razmik Arman Khosrovian
Takaharu Yaguchi
Hiroaki Yoshimura
Takashi Matsubara
AI4CE
27
0
0
15 Oct 2024
Human-Robot Cooperative Distribution Coupling for Hamiltonian-Constrained Social Navigation
Human-Robot Cooperative Distribution Coupling for Hamiltonian-Constrained Social Navigation
Weizheng Wang
Chao Yu
Yu Wang
Byung-Cheol Min
176
2
0
20 Sep 2024
Data-driven identification of latent port-Hamiltonian systems
Data-driven identification of latent port-Hamiltonian systems
J. Rettberg
Jonas Kneifl
Julius Herb
Patrick Buchfink
Jörg Fehr
B. Haasdonk
PINN
34
2
0
15 Aug 2024
Port-Hamiltonian Neural ODE Networks on Lie Groups For Robot Dynamics
  Learning and Control
Port-Hamiltonian Neural ODE Networks on Lie Groups For Robot Dynamics Learning and Control
T. Duong
Abdullah Altawaitan
Jason Stanley
Nikolay Atanasov
31
10
0
17 Jan 2024
Physics-Informed Multi-Agent Reinforcement Learning for Distributed Multi-Robot Problems
Physics-Informed Multi-Agent Reinforcement Learning for Distributed Multi-Robot Problems
Eduardo Sebastián
T. Duong
Nikolay Atanasov
Eduardo Montijano
C. Sagüés
31
3
0
30 Dec 2023
How to Learn and Generalize From Three Minutes of Data:
  Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential
  Equations
How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations
Franck Djeumou
Cyrus Neary
Ufuk Topcu
DiffM
24
8
0
10 Jun 2023
Neural Networks with Physics-Informed Architectures and Constraints for
  Dynamical Systems Modeling
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
Franck Djeumou
Cyrus Neary
Eric Goubault
S. Putot
Ufuk Topcu
PINN
AI4CE
42
68
0
14 Sep 2021
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable
  Contact Models
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models
Yaofeng Desmond Zhong
Biswadip Dey
Amit Chakraborty
52
34
0
12 Feb 2021
Lagrangian Neural Networks
Lagrangian Neural Networks
M. Cranmer
S. Greydanus
Stephan Hoyer
Peter W. Battaglia
D. Spergel
S. Ho
PINN
139
424
0
10 Mar 2020
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