Elie Hachem

TL;DR: The present implementation of stabilization finite element methods for the resolution of the 3D time-dependent incompressible Navier-Stokes equations is able to exhibit good stability and accuracy properties for high Reynolds number flows with unstructured meshes.

TL;DR: Deep reinforcement learning (DRL) has been used in a wide range of physics and engineering domains for its ability to solve decision-making problems that were previously out of reach due to a combination of nonlinearity and high dimensionality as discussed by the authors.

TL;DR: Understanding of DRL capabilities along with state-of-the-art applications in fluid dynamics to researchers wishing to address new problems with these methods is provided.

TL;DR: In this article, an artificial neural network trained through DRL is able to generate optimal shapes on its own, without any prior knowledge and in a constrained time, and the optimization process itself is agnostic to details of the use case, and thus their work paves the way to new generic shape optimization strategies both in fluid mechanics, and more generally in any domain where a relevant reward function can be defined.

TL;DR: It is shown that anisotropic meshes with highly stretched elements can be used to compute high Reynolds number flows and it will be shown that boundary layers, flow detachments and all vortices are well captured automatically by the mesh.