Recurrent Deep Kernel Learning of Dynamical Systems
<|reference_start|>Recurrent Deep Kernel Learning of Dynamical Systems: 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.<|reference_end|>
@article{botteghi2024recurrent,
    title={Recurrent Deep Kernel Learning of Dynamical Systems},
    author={Nicol`o Botteghi, Paolo Motta, Andrea Manzoni, Paolo Zunino, Mengwu
  Guo},
    journal={arXiv preprint arXiv:2405.19785},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19785},
    primaryClass={cs.LG stat.ML}
}