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 …

Feature attribution methods (AMs) are a simple means to provide explanations for the
predictions of black-box models such as neural networks. Due to their conceptual …

This article provides a comprehensive overview of noise prediction and mitigation
methodologies within the context of rocket launch events. The first section presents noise …

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 …

The paper reflects on the future role of artificial intelligence (AI) in scientific research, with a
special focus on turbulence studies, and examines the evolution of AI, particularly through …

For the last 140 years, the mechanisms of transport and dissipation of energy in a turbulent
flow have not been completely understood due to the complexity of this phenomenon. The …

This study presents a novel application of data-driven resolvent analysis algorithm for flow
control. The objective is to identify key coherent structures connected to regions of the flow …

The present study investigates streamwise ($\overline {u^ 2} $) energy-transfer mechanisms
in the inner and outer regions of turbulent boundary layers (TBLs). Particular focus is placed …

Technological advancements have substantially increased computational power and data
availability, enabling the application of powerful machine-learning (ML) techniques across …

This paper implements a spatiotemporal neural network architecture based on the U-Net
prototype with four branches, UBranch, to perform both spatial reconstruction and temporal …