This study addresses the computational challenges in fluid flow simulations arising from
demanding computational grids, required to capture the temporal and length scales …

The amalgamation of machine learning algorithms (ML) with computational fluid dynamics
(CFD) represents a promising frontier for the advancement of fluid dynamics research …

For active flow control (AFC), several frameworks have been developed to enable dynamic
interactions between deep reinforcement learning (DRL) agents and computational fluids …

High-Performance Computing (HPC) systems provide input/output (IO) performance growing
relatively slowly compared to peak computational performance and have limited storage …

Super-resolution reconstruction (SR) of turbulent flow fields with high physical fidelity from
low-resolution turbulence data is a novel and cost-effective way in a turbulence study …

Albany is a parallel C++ finite element library for solving forward and inverse problems
involving partial differential equations (PDEs). In this paper we introduce PyAlbany, a newly …

Recent years have seen many successful applications of machine learning (ML) to facilitate
fluid dynamic computations. As simulations grow, generating new training datasets for …

We propose a method for reducing the spatial discretization error of coarse computational
fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using …

The surface pressure and flow field of rectangular cylinders are of great importance in
aerodynamic analyses of the cylinders. In general, it is easy to obtain one side of the …

The occasion of this paper is to improve the body posture during exercise. The AI based
smart system to suggest better body posture by live image and video sensing is …