Lipschitz Stabilised Autoencoders in Parameter Identification of Dynamical Systems
Résumé
The present work deals with data-driven modelling. Given a set of partial noisy observations of a dynamical system, we investigate using the Lipschitz stable auto-encoder to perform an intrinsic dimension estimation to understand how many parameters are responsible for the observed variability. By incorporating the information of the intrinsic dimensionality, we investigate a data-driven model improve the classical parameter identification method.