Data-driven Nonlinear Parametric Model Order Reduction Framework using Deep Hierarchical Variational Autoencoder

A data-driven parametric model order reduction (MOR) method using a deep artificial neural network is proposed. The present network, which is the least-squares hierarchical variational autoencoder (LSH-VAE), is capable of performing nonlinear MOR for the parametric interpolation of a nonlinear dynam...

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Bibliographische Detailangaben

Hauptverfasser:	Lee, SiHun, Lee, Sangmin, Jang, Kijoo, Cho, Haeseong, Shin, SangJoon
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Physics - Data Analysis, Statistics and Probability Physics - Fluid Dynamics
Online-Zugang:	Volltext bestellen
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