gLaSDI: Parametric physics-informed greedy latent space dynamics identification

A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical systems. In the proposed gLaSDI framework, an autoencoder discovers intrinsic...

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Vydané v:	Journal of computational physics Ročník 489; s. 112267
Hlavní autori:	He, Xiaolong, Choi, Youngsoo, Fries, William D., Belof, Jonathan L., Chen, Jiun-Shyan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Inc 15.09.2023 Elsevier
Predmet:	Adaptive sampling Autoencoders MATHEMATICS AND COMPUTING Nonlinear dynamical systems Physics-informed greedy algorithm Reduced order model Regression-based dynamics identification Physics-informed greedy algorithm Nonlinear dynamical systems Adaptive sampling Reduced order model Autoencoders Regression-based dynamics identification
ISSN:	0021-9991, 1090-2716
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