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Communication Dans Un Congrès Année : 2023

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.
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Dates et versions

hal-04398536 , version 1 (16-01-2024)

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  • HAL Id : hal-04398536 , version 1

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Haibo Liu, Damiano Lombardi, Muriel Boulakia. Lipschitz Stabilised Autoencoders in Parameter Identification of Dynamical Systems. 10th International Congress on Industrial and Applied Mathematics (ICIAM 2023), Aug 2023, Tokyo, Japan. ⟨hal-04398536⟩
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