The use of data-driven methods in fluid mechanics has surged dramatically in recent years
due to their capacity to adapt to the complex and multi-scale nature of turbulent flows, as …

Many mechanical engineering applications call for multiscale computational modeling and
simulation. However, solving for complex multiscale systems remains computationally …

Despite its great scientific and technological importance, wall-bounded turbulence is an
unresolved problem in classical physics that requires new perspectives to be tackled. One of …

This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …

The problem of classifying turbulent environments from partial observation is key for some
theoretical and applied fields, from engineering to earth observation and astrophysics, eg, to …

The current revolution in the field of machine learning is leading to many interesting
developments in a wide range of areas, including fluid mechanics. Fluid mechanics, and …

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 …

Complex spatial and temporal structures are inherent characteristics of turbulent fluid flows
and comprehending them poses a major challenge. This comprehension necessitates an …

Chaos and turbulence are complex physical phenomena, yet a precise definition of the
complexity measure that quantifies them is still lacking. In this work, we consider the relative …

Events of extreme intensity in turbulent flows from atmospheric to industrial scales have a
strong social and economic impact, and hence there is a need to develop models and …