Multi-domain encoder–decoder neural networks for latent data assimilation in dynamical systems

High-dimensional dynamical systems often require computationally intensive physics-based simulations, making full physical space data assimilation impractical. Latent data assimilation methods perform assimilation in reduced-order latent space for efficiency but struggle with complex, nonlinear stat...

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Vydáno v:	Computer methods in applied mechanics and engineering Ročník 430; s. 117201
Hlavní autoři:	Cheng, Sibo, Zhuang, Yilin, Kahouadji, Lyes, Liu, Che, Chen, Jianhua, Matar, Omar K., Arcucci, Rossella
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.10.2024 Elsevier
Témata:	Computer Science Data assimilation Data fusion Deep learning Dynamical systems Environmental Sciences Nonlinear Sciences Physics Deep learning Data fusion Dynamical systems Data assimilation Data assimilation Deep learning Data fusion Dynamical systems
ISSN:	0045-7825, 1879-2138
On-line přístup:	Získat plný text
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