q-Poisson Bases and q-Poisson Curves

Liu, Yanhong; Cui, Zhenlu; Zeng, Xiaoming; Zeng, Ying

doi:10.12783/dtcse/iceiti2017/18861

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…In this work, we have also investigated adding a longshort-term-memory (LSTM) network [49] to handle the temporal information, although the significantly increased memory requirements of the new architecture limited its accuracy. Future work will aim at assessing more complicated architectures involving better downsampling, as in the U-net confgiration [50], or more efficient temporal networks such as temporal CNNs [51] or transformers [52].…”

Section: Discussionmentioning

confidence: 99%

Predicting Coherent Turbulent Structures via Deep Learning

et al. 2022

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Turbulent flow is widespread in many applications, such as airplane wings or turbine blades. Such flow is highly chaotic and impossible to predict far into the future. Some regions exhibit a coherent physical behavior in turbulent flow, satisfying specific properties; these regions are denoted as coherent structures. This work considers structures connected with the Reynolds stresses, which are essential quantities for modeling and understanding turbulent flows. Deep-learning techniques have recently had promising results for modeling turbulence, and here we investigate their capabilities for modeling coherent structures. We use data from a direct numerical simulation (DNS) of a turbulent channel flow to train a convolutional neural network (CNN) and predict the number and volume of the coherent structures in the channel over time. Overall, the performance of the CNN model is very good, with a satisfactory agreement between the predicted geometrical properties of the structures and those of the reference DNS data.

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Section: Discussionmentioning

confidence: 99%