Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems
Author:
Affiliation:
1. Department of Computer Science, University of Pisa, Pisa, Italy
2. Department of Cognitive Robotics, Delft University of Technology, Delft, The Netherlands
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Control and Optimization,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/7782633/10411713/10365513.pdf?arnumber=10365513
Reference30 articles.
1. Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems
2. Navier?Stokes Equations: Controllability by Means of Low Modes Forcing
3. Model-Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges
4. Physics-constrained, low-dimensional models for magnetohydrodynamics: First-principles and data-driven approaches
5. Hamiltonian neural networks;Greydanus
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4. Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems;IEEE Control Systems Letters;2024
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