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20182025
most citedA robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded

43 citations

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Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing physics.flu-dynShow all

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physics.flu-dyn2023★ 24 cited

Physics-informed neural networks for unsteady incompressible flows with time-dependent moving boundaries

Yongzheng Zhu, Weizhen Kong, Jian Deng +1

Physics-informed neural networks (PINNs) employed in fluid mechanics deal primarily with stationary boundaries. This hinders the capability to address a wide range of flow problems…

physics.flu-dyn2021★ 43 cited

A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded

Qi Gao, Hongtao Lin, Han Tu +4

Particle image velocimetry (PIV) is essential in experimental fluid dynamics. In the current work, we propose a new velocity field estimation paradigm, which achieves a synergetic…