Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows
نویسندگان
چکیده
We investigate the capability of neural network-based model order reduction, i.e., autoencoder (AE), for fluid flows. As an example model, AE which comprises a convolutional network and multi-layer perceptrons is considered in this study. The assessed with four canonical flows, namely: (1) two-dimensional cylinder wake, (2) its transient process, (3) NOAA sea surface temperature, (4) $y-z$ sectional field turbulent channel flow, terms number latent modes, choice nonlinear activation functions, weights contained model. find that models are sensitive against aforementioned parameters depending on target Finally, we foresee extensional applications perspectives machine learning based reduction numerical experimental studies dynamics community.
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ژورنال
عنوان ژورنال: SN computer science
سال: 2021
ISSN: ['2661-8907', '2662-995X']
DOI: https://doi.org/10.1007/s42979-021-00867-3