Recurrent Deep Kernel Learning of Dynamical Systems
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866912112653107200 |
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| author | Botteghi, Nicolò Motta, Paolo Manzoni, Andrea Zunino, Paolo Guo, Mengwu |
| author_facet | Botteghi, Nicolò Motta, Paolo Manzoni, Andrea Zunino, Paolo Guo, Mengwu |
| contents | Digital twins require computationally-efficient reduced-order models (ROMs) that can accurately describe complex dynamics of physical assets. However, constructing ROMs from noisy high-dimensional data is challenging. In this work, we propose a data-driven, non-intrusive method that utilizes stochastic variational deep kernel learning (SVDKL) to discover low-dimensional latent spaces from data and a recurrent version of SVDKL for representing and predicting the evolution of latent dynamics. The proposed method is demonstrated with two challenging examples -- a double pendulum and a reaction-diffusion system. Results show that our framework is capable of (i) denoising and reconstructing measurements, (ii) learning compact representations of system states, (iii) predicting system evolution in low-dimensional latent spaces, and (iv) quantifying modeling uncertainties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19785 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Recurrent Deep Kernel Learning of Dynamical Systems Botteghi, Nicolò Motta, Paolo Manzoni, Andrea Zunino, Paolo Guo, Mengwu Machine Learning Digital twins require computationally-efficient reduced-order models (ROMs) that can accurately describe complex dynamics of physical assets. However, constructing ROMs from noisy high-dimensional data is challenging. In this work, we propose a data-driven, non-intrusive method that utilizes stochastic variational deep kernel learning (SVDKL) to discover low-dimensional latent spaces from data and a recurrent version of SVDKL for representing and predicting the evolution of latent dynamics. The proposed method is demonstrated with two challenging examples -- a double pendulum and a reaction-diffusion system. Results show that our framework is capable of (i) denoising and reconstructing measurements, (ii) learning compact representations of system states, (iii) predicting system evolution in low-dimensional latent spaces, and (iv) quantifying modeling uncertainties. |
| title | Recurrent Deep Kernel Learning of Dynamical Systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.19785 |