Deep learning in turbulent convection networks.

ABSTRACT: We explore heat transport properties of turbulent Rayleigh-Bénard convection in horizontally extended systems by using deep-learning algorithms that greatly reduce the number of degrees of freedom. Particular attention is paid to the slowly evolving turbulent superstructures-so called because they are larger in extent than the height of the convection layer-which appear as temporal patterns of ridges of hot upwelling and cold downwelling fluid, including defects where the ridges merge or end. The machine-learning algorithm trains a deep convolutional neural network (CNN) with U-shaped architecture, consisting of a contraction and a subsequent expansion branch, to reduce the complex 3D turbulent superstructure to a temporal planar network in the midplane of the layer. This results in a data

SUBMITTER: Fonda E 

PROVIDER: S-EPMC6500159 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature