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Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent
  Dynamical Systems

Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems

Physical Review E (PRE), 2021
16 July 2021
Shaan Desai
M. Mattheakis
David Sondak
P. Protopapas
Stephen J. Roberts
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems"

41 / 41 papers shown
Unlocking the Invisible Urban Traffic Dynamics under Extreme Weather: A New Physics-Constrained Hamiltonian Learning Algorithm
Unlocking the Invisible Urban Traffic Dynamics under Extreme Weather: A New Physics-Constrained Hamiltonian Learning Algorithm
Xuhui Lin
Qiuchen Lu
114
0
0
03 Dec 2025
Learning Physically Consistent Lagrangian Control Models Without Acceleration Measurements
Learning Physically Consistent Lagrangian Control Models Without Acceleration Measurements
Ibrahim Laiche
Mokrane Boudaoud
Patrick Gallinari
Pascal Morin
205
0
0
02 Dec 2025
Pixel-level Quality Assessment for Oriented Object Detection
Pixel-level Quality Assessment for Oriented Object Detection
Yunhui Zhu
Buliao Huang
ObjD
514
0
0
11 Nov 2025
Identifiable learning of dissipative dynamics
Identifiable learning of dissipative dynamics
Aiqing Zhu
Beatrice W. Soh
Grigorios A. Pavliotis
Qianxiao Li
PINNAI4CE
432
2
0
28 Oct 2025
Let Physics Guide Your Protein Flows: Topology-aware Unfolding and Generation
Let Physics Guide Your Protein Flows: Topology-aware Unfolding and Generation
Yogesh Verma
Markus Heinonen
Vikas Garg
DiffMAI4CE
488
0
0
29 Sep 2025
Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
Luke McLennan
Yi Wang
Ryan Farell
Minh Nguyen
Chandrajit Bajaj
174
1
0
08 Sep 2025
Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems
Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems
Quercus Hernandez
Max Win
Thomas C. O'Connor
Paulo E. Arratia
Nathaniel Trask
AI4CE
262
2
0
18 Aug 2025
Meta-learning Structure-Preserving Dynamics
Meta-learning Structure-Preserving Dynamics
Cheng Jing
Uvini Balasuriya Mudiyanselage
Woojin Cho
Minju Jo
A. Gruber
Kookjin Lee
AI4CE
161
1
0
15 Aug 2025
On the Generalization of Data-Assisted Control in port-Hamiltonian Systems (DAC-pH)
On the Generalization of Data-Assisted Control in port-Hamiltonian Systems (DAC-pH)
Mostafa Eslami
Maryam Babazadeh
226
2
0
08 Jun 2025
Rapid training of Hamiltonian graph networks using random features
Rapid training of Hamiltonian graph networks using random features
Atamert Rahma
Chinmay Datar
Ana Cukarska
Felix Dietrich
AI4CE
313
0
0
06 Jun 2025
Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics
Learning mechanical systems from real-world data using discrete forced Lagrangian dynamics
Martine Dyring Hansen
Elena Celledoni
Benjamin Kwanen Tampley
PINN
259
2
0
26 May 2025
Stable Port-Hamiltonian Neural Networks
Stable Port-Hamiltonian Neural Networks
Fabian J. Roth
Dominik K. Klein
Maximilian Kannapinn
Jan Peters
Oliver Weeger
559
11
0
04 Feb 2025
Symplectic Neural Flows for Modeling and Discovery
Symplectic Neural Flows for Modeling and Discovery
Priscilla Canizares
Davide Murari
Carola-Bibiane Schönlieb
Ferdia Sherry
Zakhar Shumaylov
PINN
381
6
0
21 Dec 2024
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
437
4
0
15 Dec 2024
Training Hamiltonian neural networks without backpropagation
Training Hamiltonian neural networks without backpropagation
Atamert Rahma
Chinmay Datar
Felix Dietrich
342
5
0
26 Nov 2024
Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian
  Neural Networks
Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
G. J. E. van Otterdijk
S. Moradi
S. Weiland
R. Tóth
N. O. Jaensson
M. Schoukens
166
0
0
08 Nov 2024
Human-Robot Cooperative Distribution Coupling for Hamiltonian-Constrained Social Navigation
Human-Robot Cooperative Distribution Coupling for Hamiltonian-Constrained Social NavigationIEEE International Conference on Robotics and Automation (ICRA), 2024
Weizheng Wang
Chao Yu
Yu Wang
Byung-Cheol Min
921
3
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
409
6
0
15 Aug 2024
Physics-Constrained Learning for PDE Systems with Uncertainty Quantified
  Port-Hamiltonian Models
Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models
Kaiyuan Tan
Peilun Li
Thomas Beckers
AI4CE
308
8
0
17 Jun 2024
Predicting Ship Responses in Different Seaways using a Generalizable
  Force Correcting Machine Learning Method
Predicting Ship Responses in Different Seaways using a Generalizable Force Correcting Machine Learning Method
K. Marlantes
P. Bandyk
Kevin J. Maki
271
15
0
13 May 2024
Neural Operators Meet Energy-based Theory: Operator Learning for
  Hamiltonian and Dissipative PDEs
Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs
Yusuke Tanaka
Takaharu Yaguchi
Tomoharu Iwata
N. Ueda
AI4CE
455
0
0
14 Feb 2024
Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey
  on Structural Mechanics Applications
Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics ApplicationsData-Centric Engineering (DCE), 2023
M. Haywood-Alexander
Wei Liu
Kiran Bacsa
Zhilu Lai
Eleni Chatzi
AI4CE
432
29
0
31 Oct 2023
Separable Hamiltonian Neural Networks
Separable Hamiltonian Neural Networks
Zi-Yu Khoo
Dawen Wu
Jonathan Sze Choong Low
Stéphane Bressan
365
3
0
03 Sep 2023
Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired
  Embeddings for Nonlinear Canonical Hamiltonian Dynamics
Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics
P. Goyal
Süleyman Yıldız
P. Benner
233
6
0
26 Aug 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 EquationsConference on Robot Learning (CoRL), 2023
Franck Djeumou
Cyrus Neary
Ufuk Topcu
DiffM
325
15
0
10 Jun 2023
Reversible and irreversible bracket-based dynamics for deep graph neural
  networks
Reversible and irreversible bracket-based dynamics for deep graph neural networksNeural Information Processing Systems (NeurIPS), 2023
A. Gruber
Kookjin Lee
N. Trask
AI4CE
323
23
0
24 May 2023
Learning Switching Port-Hamiltonian Systems with Uncertainty
  Quantification
Learning Switching Port-Hamiltonian Systems with Uncertainty QuantificationIFAC-PapersOnLine (IFAC-PapersOnLine), 2023
Thomas Beckers
Tom Z. Jiahao
George J. Pappas
237
3
0
15 May 2023
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with
  Physics Prior
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics PriorIEEE Conference on Decision and Control (CDC), 2022
Thomas Beckers
Jacob H. Seidman
P. Perdikaris
George J. Pappas
PINN
276
34
0
15 May 2023
Pseudo-Hamiltonian system identification
Pseudo-Hamiltonian system identificationJournal of Computational Dynamics (J. Comput. Dyn.), 2023
Sigurd Holmsen
Sølve Eidnes
S. Riemer-Sørensen
397
6
0
09 May 2023
Physics-Informed Learning Using Hamiltonian Neural Networks with Output
  Error Noise Models
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise ModelsIFAC-PapersOnLine (IFAC-PapersOnLine), 2023
Sarvin Moradi
N. Jaensson
Roland Tóth
Maarten Schoukens
PINN
280
10
0
02 May 2023
Pseudo-Hamiltonian neural networks for learning partial differential
  equations
Pseudo-Hamiltonian neural networks for learning partial differential equationsJournal of Computational Physics (JCP), 2023
Sølve Eidnes
K. Lye
444
17
0
27 Apr 2023
Compositional Learning of Dynamical System Models Using Port-Hamiltonian
  Neural Networks
Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural NetworksConference on Learning for Dynamics & Control (L4DC), 2022
Cyrus Neary
Ufuk Topcu
PINNAI4CE
317
23
0
01 Dec 2022
Thermodynamics of learning physical phenomena
Thermodynamics of learning physical phenomenaArchives of Computational Methods in Engineering (ACME), 2022
Elías Cueto
Francisco Chinesta
AI4CE
398
29
0
26 Jul 2022
Pseudo-Hamiltonian Neural Networks with State-Dependent External Forces
Pseudo-Hamiltonian Neural Networks with State-Dependent External Forces
Sølve Eidnes
Alexander J. Stasik
Camilla Sterud
Eivind Bøhn
S. Riemer-Sørensen
461
24
0
06 Jun 2022
Learning reversible symplectic dynamics
Learning reversible symplectic dynamicsConference on Learning for Dynamics & Control (L4DC), 2022
Riccardo Valperga
K. Webster
Victoria G Klein
D. Turaev
J. Lamb
AI4CE
148
17
0
26 Apr 2022
Thermodynamics-informed graph neural networks
Thermodynamics-informed graph neural networksIEEE Transactions on Artificial Intelligence (IEEE TAI), 2022
Quercus Hernandez
Alberto Badías
Francisco Chinesta
Elías Cueto
AI4CEPINN
314
53
0
03 Mar 2022
Deconstructing the Inductive Biases of Hamiltonian Neural Networks
Deconstructing the Inductive Biases of Hamiltonian Neural NetworksInternational Conference on Learning Representations (ICLR), 2022
Nate Gruver
Marc Finzi
Samuel Stanton
A. Wilson
AI4CE
273
50
0
10 Feb 2022
Noether Networks: Meta-Learning Useful Conserved Quantities
Noether Networks: Meta-Learning Useful Conserved QuantitiesNeural Information Processing Systems (NeurIPS), 2021
Ferran Alet
Dylan D. Doblar
Allan Zhou
J. Tenenbaum
Kenji Kawaguchi
Chelsea Finn
279
31
0
06 Dec 2021
One-Shot Transfer Learning of Physics-Informed Neural Networks
One-Shot Transfer Learning of Physics-Informed Neural Networks
Shaan Desai
M. Mattheakis
H. Joy
P. Protopapas
Stephen J. Roberts
PINNAI4CE
390
74
0
21 Oct 2021
Structure-preserving Sparse Identification of Nonlinear Dynamics for
  Data-driven Modeling
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling
Kookjin Lee
Nathaniel Trask
P. Stinis
245
38
0
11 Sep 2021
Hamiltonian neural networks for solving equations of motion
Hamiltonian neural networks for solving equations of motionPhysical Review E (PRE), 2020
M. Mattheakis
David Sondak
Akshunna S. Dogra
P. Protopapas
640
90
0
29 Jan 2020
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