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2312.06256
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Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems
11 December 2023
Marco Lepri
Davide Bacciu
Cosimo Della Santina
AI4CE
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Papers citing
"Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems"
8 / 8 papers shown
Title
Soft Synergies: Model Order Reduction of Hybrid Soft-Rigid Robots via Optimal Strain Parameterization
Abdulaziz Alkayas
A. Mathew
Daniel Feliú Talegon
Ping Deng
T. G. Thuruthel
F. Renda
98
4
0
20 Feb 2025
A Riemannian Framework for Learning Reduced-order Lagrangian Dynamics
Katharina Friedl
Noémie Jaquier
Jens Lundell
Tamim Asfour
Danica Kragic
AI4CE
26
0
0
24 Oct 2024
Data-driven identification of latent port-Hamiltonian systems
J. Rettberg
Jonas Kneifl
Julius Herb
Patrick Buchfink
Jörg Fehr
B. Haasdonk
PINN
19
2
0
15 Aug 2024
Data-driven Model Reduction for Soft Robots via Lagrangian Operator Inference
Harsh Sharma
Iman Adibnazari
Jacobo Cervera-Torralba
M. Tolley
Boris Kramer
34
0
0
11 Jul 2024
Using Spectral Submanifolds for Nonlinear Periodic Control
Florian Mahlknecht
J. I. Alora
Shobhit Jain
Edward Schmerling
Riccardo Bonalli
George Haller
Marco Pavone
65
6
0
14 Sep 2022
Applying Machine Learning to Study Fluid Mechanics
Steven L. Brunton
PINN
AI4CE
37
95
0
05 Oct 2021
Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models
M. Lutter
Jan Peters
PINN
AI4CE
29
38
0
05 Oct 2021
Lagrangian Neural Networks
M. Cranmer
S. Greydanus
Stephan Hoyer
Peter W. Battaglia
D. Spergel
S. Ho
PINN
121
419
0
10 Mar 2020
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