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, a least-squares hierarchical variational autoencoder (LSH-VAE), is capable of performing nonlinear MOR for the parametric interpolation of a nonlinear dynamic system w...

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Vydané v:	Engineering with computers Ročník 40; číslo 4; s. 2385 - 2400
Hlavní autori:	Lee, SiHun, Lee, Sangmin, Jang, Kijoo, Cho, Haeseong, Shin, SangJoon
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.08.2024 Springer Nature B.V
Predmet:	Accuracy Artificial neural networks CAE) and Design Calculus of Variations and Optimal Control; Optimization Classical Mechanics Computer Science Computer-Aided Engineering (CAD Control Degrees of freedom Dynamical systems Fluid flow Fluid-structure interaction Interpolation Limit cycle oscillations Math. Applications in Chemistry Mathematical and Computational Engineering Model reduction Nonlinear dynamics Nonlinear systems Original Article Systems Theory Three dimensional analysis Three dimensional flow Multiphysics analysis Variational autoencoder Parametric interpolation Nonlinear model order reduction Machine learning
ISSN:	0177-0667, 1435-5663
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