.png){kind=icon}
.png){kind=icon}
Return
Data -driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network
.png){kind=icon}
OA
AI
DOI：10.1016/j.cma.2020.113000.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En
Keywords:
ARTIFICIAL-INTELLIGENCE
INFLOW FORECASTS
NEURAL-NETWORK
ADAPTIVITY
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.3
Papers:
1.3W
Citations:
5.6W
