In the following paper, we discuss the exhaustive use and implementation of stabilization
finite element methods for the resolution of the 3D time-dependent incompressible Navier …

Metric tensors play a key role to control the generation of unstructured anisotropic meshes.
In practice, the most well established error analysis enables to calculate a metric tensor on …

This research gauges the ability of deep reinforcement learning (DRL) techniques to assist
the control of conjugate heat transfer systems governed by the coupled Navier–Stokes and …

This papers considers the topology optimization of duct flows governed by the three-
dimensional steady state Navier–Stokes equations, using anisotropic mesh adaptation to …

This paper presents advancements toward a monolithic solution procedure and anisotropic
mesh adaptation for the numerical solution of fluid–structure interaction with complex …

Abstract The Physical Informed Neural Networks (PINN) model is one of the emerging and
promising Deep Learning approaches to predict physical phenomena governed by PDEs …

The availability of accurate and efficient numerical simulation tools has become of utmost
importance for the design and optimization phases of existing industrial processes. The …

We consider the numerical simulation of conjugate heat transfer, incompressible turbulent
flows for multicomponents systems using a stabilized finite element method. We present an …

This article proposes a numerical strategy to simulate the mass transport by surface and
lattice diffusion into a granular packing. This strategy is based on two cornerstones. First, the …

A numerical implementation of a three-dimensional (3D) cellular automaton (CA)–finite
element (FE) model has been developed for the prediction of solidification grain structures …