124 citations · 269 across the 17 of their papers we have counts for
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physics.flu-dyn2021★ 1 cited
Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks
Shengyu Chen, Shervin Sammak, Peyman Givi +2
Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers. Large eddy simulation (LES) is an alte…
physics.flu-dyn2021★ 124 cited
A Quantum Inspired Approach to Exploit Turbulence Structures
Nikita Gourianov, Michael Lubasch, Sergey Dolgov +5
Understanding turbulence is the key to our comprehension of many natural and technological flow processes. At the heart of this phenomenon lies its intricate multi-scale nature, de…