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A General Framework for Structured Learning of Mechanical Systems

A General Framework for Structured Learning of Mechanical Systems

22 February 2019
Jayesh K. Gupta
Kunal Menda
Zachary Manchester
Mykel J. Kochenderfer
    AI4CE
ArXivPDFHTML

Papers citing "A General Framework for Structured Learning of Mechanical Systems"

14 / 14 papers shown
Title
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
54
10
0
17 Jan 2024
Discovering interpretable Lagrangian of dynamical systems from data
Discovering interpretable Lagrangian of dynamical systems from data
Tapas Tripura
S. Chakraborty
32
4
0
09 Feb 2023
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
3DH
AI4CE
51
8
0
23 Sep 2022
Lagrangian Density Space-Time Deep Neural Network Topology
Lagrangian Density Space-Time Deep Neural Network Topology
B. Bishnoi
PINN
25
1
0
30 Jun 2022
Deconstructing the Inductive Biases of Hamiltonian Neural Networks
Deconstructing the Inductive Biases of Hamiltonian Neural Networks
Nate Gruver
Marc Finzi
Samuel Stanton
A. Wilson
AI4CE
26
40
0
10 Feb 2022
A Differentiable Newton-Euler Algorithm for Real-World Robotics
A Differentiable Newton-Euler Algorithm for Real-World Robotics
M. Lutter
Vallijah Subasri
Joe Watson
Frank Rudzicz
29
7
0
24 Oct 2021
Combining Physics and Deep Learning to learn Continuous-Time Dynamics
  Models
Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models
M. Lutter
Jan Peters
PINN
AI4CE
43
39
0
05 Oct 2021
Differentiable Physics Models for Real-world Offline Model-based
  Reinforcement Learning
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning
M. Lutter
Johannes Silberbauer
Joe Watson
Jan Peters
OffRL
29
33
0
03 Nov 2020
A Differentiable Newton Euler Algorithm for Multi-body Model Learning
A Differentiable Newton Euler Algorithm for Multi-body Model Learning
M. Lutter
Johannes Silberbauer
Joe Watson
Jan Peters
21
11
0
19 Oct 2020
Modeling System Dynamics with Physics-Informed Neural Networks Based on
  Lagrangian Mechanics
Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics
Manuel A. Roehrl
Thomas Runkler
Veronika Brandtstetter
Michel Tokic
Stefan Obermayer
PINN
24
77
0
29 May 2020
Lagrangian Neural Networks
Lagrangian Neural Networks
M. Cranmer
S. Greydanus
Stephan Hoyer
Peter W. Battaglia
D. Spergel
S. Ho
PINN
139
425
0
10 Mar 2020
Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and
  Control into Deep Learning
Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning
Yaofeng Desmond Zhong
Biswadip Dey
Amit Chakraborty
PINN
AI4CE
34
78
0
20 Feb 2020
Linearly Constrained Neural Networks
Linearly Constrained Neural Networks
J. Hendriks
Carl Jidling
A. Wills
Thomas B. Schon
21
34
0
05 Feb 2020
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
Yaofeng Desmond Zhong
Biswadip Dey
Amit Chakraborty
PINN
54
269
0
26 Sep 2019
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