Jacobian-free Newton–Krylov (JFNK) methods are synergistic combinations of Newton-type
methods for superlinearly convergent solution of nonlinear equations and Krylov subspace …

Deep reinforcement learning (DRL) has been applied to a variety of problems during the
past decade and has provided effective control strategies in high-dimensional and non …

We present a novel deep learning framework for flow field predictions in irregular domains
when the solution is a function of the geometry of either the domain or objects inside the …

Deep Reinforcement Learning (DRL) has recently been proposed as a methodology to
discover complex active flow control strategies [Rabault et al.,“Artificial neural networks …

The favorable convergence rates of Newton–Krylov-based solution algorithms have
increased their popularity for computational fluid dynamics applications. Unfortunately, these …

An ultra-scalable fully-implicit solver is developed for stiff time-dependent problems arising
from the hyperbolic conservation laws in nonhydrostatic atmospheric dynamics. In the …

Computational fluid dynamics (CFD) is one of the most emerging fields of fluid mechanics
used to analyze fluid flow situation. This analysis is based on simulations carried out on …

The aging of the Space Shuttle prompted significant research on possible designs for its
replacement through the years. For a long time it was believed that the replacement for this …

We study preconditioners for the iterative solution of the linear systems arising in the implicit
time integration of the compressible Navier–Stokes equations. The spatial discretization is …

An efficient gradient-based algorithm for aerodynamic shape optimization is presented. The
algorithm consists of several components, including a novel integrated geometry …