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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Veröffentlicht in:	Journal of computational physics Jg. 489; S. 112267
Hauptverfasser:	He, Xiaolong, Choi, Youngsoo, Fries, William D., Belof, Jonathan L., Chen, Jiun-Shyan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 15.09.2023 Elsevier
Schlagworte:	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
Online-Zugang:	Volltext
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