Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems

11 December 2023

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