The 2D lid-driven cavity problem revisited

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How researchers see this paper

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Understanding and mitigating gradient pathologies in physics-informed neural networks (2020, methodology): “In this example, we chosen a relatively simple case corresponding to a Reynolds number of Re = 100, for which it is well understood that the flow quickly converges to an incompressible steady-state solution [51].”
Factors of Safety for Richardson Extrapolation (2010, background): “3 2D driven cavity [20] Re=1000 Maximum/minimum Of stream-function, vorticity 4 grids 1 NB [21]”
Isogeometric Analysis and error estimates for high order partial differential equations in Fluid Dynamics (2014, methodology): “As reported in [11, 25], the number of regions in which the vortexes develop increases with the Reynolds number, specifically at the corners of the domain.”
Physics-Informed Neural Operator for Learning Partial Differential Equations (2021, unknown): “— Lid cavity flow : In this case, we assume the no-slip boundary condition where u ( x , t ) = ( 0 , 0 ) at left, bottom, and right walls and u ( x , t ) = ( 1 , 0 ) on top, similar to Bruneau and Saad [53].”