Abstract Machine learning (ML)-based techniques have found significant impact in many
fields of engineering and sciences, where data-sets are available from experiments and …

In this work, artificial neural network-based nonlinear algebraic models (ANN-NAMs) are
developed for the subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence at …

Deconvolutional artificial neural network (DANN) models are developed for subgrid-scale
(SGS) stress in large eddy simulation (LES) of turbulence. The filtered velocities at different …

In this work, the subgrid-scale (SGS) stress and the SGS heat flux of compressible isotropic
turbulence are modeled by an artificial neural network (ANN) mixed model (ANNMM), which …

Most investigations of turbulent flows in academic studies and industrial applications use
turbulence models. Out of the different turbulence modeling approaches Reynolds stress …

In this work, subgrid-scale (SGS) stress and SGS heat flux of compressible isotropic
turbulence are reconstructed by a spatially multi-scale artificial neural network (SMSANN) …

Transition modeling represents one of the key challenges in computational fluid dynamics.
While numerical efforts were traditionally devoted to either improving Reynolds-averaged …

Reynolds-averaged Navier–Stokes (RANS) models are known to be inaccurate in complex
flows, for instance, laminar-turbulent transition, and RANS uncertainty quantification (UQ) is …

It is well known that the Boussinesq turbulent-viscosity hypothesis can introduce uncertainty
in predictions for complex flow features such as separation, reattachment, and laminar …

The application of machine learning (ML) algorithms to turbulence modeling has shown
promise over the last few years, but their application has been restricted to eddy viscosity …