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GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder
May 7, 2024, 4:45 a.m. | Christophe Bonneville, Youngsoo Choi, Debojyoti Ghosh, Jonathan L. Belof
cs.LG updates on arXiv.org arxiv.org
Abstract: Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are accurate but faster than full order models (FOMs). Recently, machine learning advances have enabled the creation of non-linear projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI maps full-order PDE solutions to a latent space using autoencoders and learns the system of ODEs governing the latent space dynamics. By interpolating …
abstract advances arxiv autoencoder cs.ce cs.lg cs.na development differential dynamics faster identification linear machine machine learning math.na non-linear process projection space through type
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