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  3. 2002.08860
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Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and
  Control into Deep Learning
v1v2v3 (latest)

Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning

20 February 2020
Yaofeng Desmond Zhong
Biswadip Dey
Amit Chakraborty
    PINNAI4CE
ArXiv (abs)PDFHTML

Papers citing "Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning"

50 / 70 papers shown
Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach
Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach
Katharina Friedl
Noémie Jaquier
Mika Liao
Danica Kragic
AI4CE
147
1
0
29 Sep 2025
Physically Plausible Multi-System Trajectory Generation and Symmetry Discovery
Physically Plausible Multi-System Trajectory Generation and Symmetry Discovery
Jiayin Liu
Yulong Yang
Vineet Bansal
Christine Allen-Blanchette
175
1
0
26 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
172
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
260
2
0
18 Aug 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
257
2
0
26 May 2025
Denoising Hamiltonian Network for Physical Reasoning
Denoising Hamiltonian Network for Physical Reasoning
Congyue Deng
Brandon Yushan Feng
Cecilia Garraffo
Alan Garbarz
Robin Walters
William T. Freeman
Leonidas Guibas
Kaiming He
AI4CE
290
7
0
10 Mar 2025
ServoLNN: Lagrangian Neural Networks Driven by Servomechanisms
ServoLNN: Lagrangian Neural Networks Driven by Servomechanisms
Brandon Johns
Zhuomin Zhou
Elahe Abdi
PINN3DV
311
0
0
27 Feb 2025
MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence
MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence
Pranav Vaidhyanathan
Aristotelis Papatheodorou
Mark T. Mitchison
Natalia Ares
Ioannis Havoutis
PINNAI4CE
478
3
0
23 Feb 2025
Stable Port-Hamiltonian Neural Networks
Stable Port-Hamiltonian Neural Networks
Fabian J. Roth
Dominik K. Klein
Maximilian Kannapinn
Jan Peters
Oliver Weeger
546
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
378
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
436
4
0
15 Dec 2024
Training Hamiltonian neural networks without backpropagation
Training Hamiltonian neural networks without backpropagation
Atamert Rahma
Chinmay Datar
Felix Dietrich
338
5
0
26 Nov 2024
Learning dissipative Hamiltonian dynamics with reproducing kernel
  Hilbert spaces and random Fourier features
Learning dissipative Hamiltonian dynamics with reproducing kernel Hilbert spaces and random Fourier featuresIFAC-PapersOnLine (IFAC-PapersOnLine), 2024
Torbjørn Smith
Olav Egeland
271
0
0
24 Oct 2024
Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems
  across Domains
Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across DomainsInternational Conference on Learning Representations (ICLR), 2024
Razmik Arman Khosrovian
Takaharu Yaguchi
Hiroaki Yoshimura
Takashi Matsubara
AI4CE
264
0
0
15 Oct 2024
Physics-Informed Regularization for Domain-Agnostic Dynamical System
  Modeling
Physics-Informed Regularization for Domain-Agnostic Dynamical System ModelingNeural Information Processing Systems (NeurIPS), 2024
Zijie Huang
Wanjia Zhao
Jingdong Gao
Ziniu Hu
Xiao Luo
Yadi Cao
Yuanzhou Chen
Yizhou Sun
Wei Wang
PINNAI4CE
229
9
0
08 Oct 2024
A Two-Stage Training Method for Modeling Constrained Systems With Neural
  Networks
A Two-Stage Training Method for Modeling Constrained Systems With Neural Networks
C. Coelho
M. F. P. Costa
L. L. Ferrás
215
2
0
05 Mar 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
454
0
0
14 Feb 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 ControlIEEE Transactions on robotics (IEEE Trans. Robot.), 2024
T. Duong
Abdullah Altawaitan
Jason Stanley
Nikolay Atanasov
376
30
0
17 Jan 2024
Learning Dissipative Neural Dynamical Systems
Learning Dissipative Neural Dynamical SystemsIEEE Control Systems Letters (L-CSS), 2023
Yuezhu Xu
S. Sivaranjani
308
8
0
27 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
212
6
0
26 Aug 2023
Learning to Predict 3D Rotational Dynamics from Images of a Rigid Body
  with Unknown Mass Distribution
Learning to Predict 3D Rotational Dynamics from Images of a Rigid Body with Unknown Mass Distribution
J. Mason
Christine Allen-Blanchette
Nicholas Zolman
Elizabeth Davison
Naomi Leonard
3DH
370
8
0
24 Aug 2023
Hamiltonian GAN
Hamiltonian GANConference on Learning for Dynamics & Control (L4DC), 2023
Christine Allen-Blanchette
GANAI4CE
256
4
0
22 Aug 2023
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian
  Graph Neural Networks
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks
S. Bishnoi
Ravinder Bhattoo
J. Jayadeva
Jignesh M. Patel
N. M. A. Krishnan
PINNAI4CE
296
3
0
11 Jul 2023
Physics-Informed Machine Learning for Modeling and Control of Dynamical
  Systems
Physics-Informed Machine Learning for Modeling and Control of Dynamical SystemsAmerican Control Conference (ACC), 2023
Truong X. Nghiem
Ján Drgoňa
Colin N. Jones
Zoltán Nagy
Roland Schwan
...
J. Paulson
Andrea Carron
Melanie Zeilinger
Wenceslao Shaw-Cortez
D. Vrabie
PINNAI4CE
292
67
0
24 Jun 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
321
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
316
23
0
24 May 2023
Uncertainty and Structure in Neural Ordinary Differential Equations
Uncertainty and Structure in Neural Ordinary Differential Equations
Katharina Ott
Michael Tiemann
Philipp Hennig
AI4CE
330
6
0
22 May 2023
Pseudo-Hamiltonian system identification
Pseudo-Hamiltonian system identificationJournal of Computational Dynamics (J. Comput. Dyn.), 2023
Sigurd Holmsen
Sølve Eidnes
S. Riemer-Sørensen
394
6
0
09 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
440
17
0
27 Apr 2023
Discovering interpretable Lagrangian of dynamical systems from data
Discovering interpretable Lagrangian of dynamical systems from dataComputer Physics Communications (CPC), 2023
Tapas Tripura
S. Chakraborty
211
6
0
09 Feb 2023
Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge
  with Data-Driven Control
Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven ControlAmerican Control Conference (ACC), 2023
Adam J. Thorpe
Cyrus Neary
Franck Djeumou
Meeko Oishi
Ufuk Topcu
334
8
0
09 Jan 2023
Physics-Informed Model-Based Reinforcement Learning
Physics-Informed Model-Based Reinforcement LearningConference on Learning for Dynamics & Control (L4DC), 2022
Adithya Ramesh
Balaraman Ravindran
328
28
0
05 Dec 2022
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
311
23
0
01 Dec 2022
Lie Group Forced Variational Integrator Networks for Learning and
  Control of Robot Systems
Lie Group Forced Variational Integrator Networks for Learning and Control of Robot SystemsConference on Learning for Dynamics & Control (L4DC), 2022
Valentin Duruisseaux
T. Duong
Melvin Leok
Nikolay Atanasov
DRLAI4CE
553
17
0
29 Nov 2022
Unravelling the Performance of Physics-informed Graph Neural Networks
  for Dynamical Systems
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical SystemsNeural Information Processing Systems (NeurIPS), 2022
A. Thangamuthu
Gunjan Kumar
S. Bishnoi
Ravinder Bhattoo
N. M. A. Krishnan
Jignesh M. Patel
AI4CEPINN
218
35
0
10 Nov 2022
Port-metriplectic neural networks: thermodynamics-informed machine
  learning of complex physical systems
Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systemsComputational Mechanics (Comput. Mech.), 2022
Quercus Hernandez
Alberto Badías
Francisco Chinesta
Elías Cueto
PINNAI4CE
482
20
0
03 Nov 2022
Approximation of nearly-periodic symplectic maps via
  structure-preserving neural networks
Approximation of nearly-periodic symplectic maps via structure-preserving neural networksScientific Reports (Sci Rep), 2022
Valentin Duruisseaux
J. Burby
Q. Tang
360
14
0
11 Oct 2022
FINDE: Neural Differential Equations for Finding and Preserving
  Invariant Quantities
FINDE: Neural Differential Equations for Finding and Preserving Invariant QuantitiesInternational Conference on Learning Representations (ICLR), 2022
Takashi Matsubara
Takaharu Yaguchi
PINN
304
11
0
01 Oct 2022
Learning Interpretable Dynamics from Images of a Freely Rotating 3D
  Rigid Body
Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body
J. Mason
Christine Allen-Blanchette
Nicholas Zolman
Elizabeth Davison
Naomi Ehrich Leonard
3DHAI4CE
455
10
0
23 Sep 2022
Enhancing the Inductive Biases of Graph Neural ODE for Modeling
  Dynamical Systems
Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
S. Bishnoi
Ravinder Bhattoo
Jignesh M. Patel
N. M. A. Krishnan
AI4CE
331
24
0
22 Sep 2022
Learning the Dynamics of Particle-based Systems with Lagrangian Graph
  Neural Networks
Learning the Dynamics of Particle-based Systems with Lagrangian Graph Neural Networks
Ravinder Bhattoo
Jignesh M. Patel
N. M. A. Krishnan
PINNAI4CE
280
26
0
03 Sep 2022
Constants of motion network
Constants of motion networkNeural Information Processing Systems (NeurIPS), 2022
M. F. Kasim
Yi Heng Lim
392
9
0
22 Aug 2022
Unifying physical systems' inductive biases in neural ODE using dynamics
  constraints
Unifying physical systems' inductive biases in neural ODE using dynamics constraints
Yi Heng Lim
M. F. Kasim
PINNAI4CE
173
6
0
03 Aug 2022
Thermodynamics of learning physical phenomena
Thermodynamics of learning physical phenomenaArchives of Computational Methods in Engineering (ACME), 2022
Elías Cueto
Francisco Chinesta
AI4CE
390
29
0
26 Jul 2022
Robust and Safe Autonomous Navigation for Systems with Learned SE(3)
  Hamiltonian Dynamics
Robust and Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics
Zhichao Li
T. Duong
Nikolay Atanasov
308
2
0
22 Jul 2022
KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates
  from Images
KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from ImagesNeurocomputing (Neurocomputing), 2022
Rembert Daems
Jeroen Taets
Francis Wyffels
Guillaume Crevecoeur
291
1
0
22 Jun 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
23
0
06 Jun 2022
A Review of Machine Learning Methods Applied to Structural Dynamics and
  Vibroacoustic
A Review of Machine Learning Methods Applied to Structural Dynamics and VibroacousticMechanical systems and signal processing (MSSP), 2022
Barbara Z Cunha
C. Droz
A. Zine
Stéphane Foulard
M. Ichchou
AI4CE
266
134
0
13 Apr 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
265
50
0
10 Feb 2022
Dissipative Hamiltonian Neural Networks: Learning Dissipative and
  Conservative Dynamics Separately
Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately
A. Sosanya
S. Greydanus
PINNAI4CE
357
46
0
25 Jan 2022
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