Recurrent Deep Kernel Learning of Dynamical Systems

• URL: http://arxiv.org/abs/2405.19785v3
• Date: Sat, 09 Nov 2024 17:58:56 GMT
• Title: Recurrent Deep Kernel Learning of Dynamical Systems
• Authors: Nicolò Botteghi, Paolo Motta, Andrea Manzoni, Paolo Zunino, Mengwu Guo,
• Abstract summary: Digital twins require computationally-efficient reduced-order models (ROMs) that can accurately describe complex dynamics of physical assets. We propose a data-driven, non-intrusive deep kernel learning (SVDKL) method to discover low-dimensional latent spaces from data. 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) modeling uncertainties.
• Score: 0.5825410941577593
• License:
• Abstract: 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.

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