We present in this paper a framework for the identification of control-affine reduced-order models (ROMs). The proposed method utilizes autoencoders (AEs) to transform the high-dimensional states, and potentially the high-dimensional inputs, into reduced latent ones suitable for control-affine state-space dynamics.
The article "Dynamical System Transfer Learning with Reduced Order Models" discusses how to enhance reinforcement learning for complex physics problems by employing reduced order models. It explores the application of transfer learning techniques to dynamical systems, aiming to improve the efficiency and effectiveness of reinforcement learning algorithms in physics-based simulations.
By Robert Etter
arXiv:2606. 05045v1 Announce Type: cross Abstract: We present in this paper a framework for the identification of control-affine reduced-order models (ROMs).
By Ali Mjalled, Martin M\"onnigmann
arXiv:2605. 01835v2 Announce Type: replace Abstract: Nonlinear coupled systems are ubiquitous in science and engineering.
By Tatsuya Naoi, Jun Ohkubo
arXiv:2609.25163v1 Announce Type: new
Abstract: The paper proposes a novel data-driven framework for designing and training a feedback linearizing controller by explicitly incorporating relative degr...
By Lakshmi Priya P. K., Andreas Schwung
arXiv:2606. 23827v1 Announce Type: cross Abstract: A data-driven method is developed for approximating value functions in deterministic optimal control problems with nonlinear control-affine dynamics.
By Mat\'ias G\'omez-Aedo, Behzad Azmi, Yuyang Huang, Dante Kalise, Karl Kunisch