The significant growth of artificial intelligence (AI) methods in machine learning (ML) and
deep learning (DL) has opened opportunities for fluid dynamics and its applications in …

Lagrangian turbulence lies at the core of numerous applied and fundamental problems
related to the physics of dispersion and mixing in engineering, biofluids, the atmosphere …

This paper investigates deep learning methods in the framework of convolutional neural
networks for reconstructing compressible turbulent flow fields. The aim is to develop …

We address the problem of data augmentation in a rotating turbulence set-up, a
paradigmatic challenge in geophysical applications. The goal is to reconstruct information in …

Computational Fluid Dynamics (CFD) plays a crucial role in investigating new physical
phenomena and exploring the principles of fluid mechanics. However, CFD numerical …

Reconstructing spatiotemporal dynamics with sparse sensor measurement is a challenging
task that is encountered in a wide spectrum of scientific and engineering applications. The …

Due to costs and practical constraints, field campaigns in the atmospheric boundary layer
typically only measure a fraction of the atmospheric volume of interest. Machine learning …

Structural health monitoring (SHM) plays a crucial role in ensuring the safety of infrastructure
in general, especially critical infrastructure such as bridges. SHM systems allow the real-time …

Convolutional neural networks (CNN) have been widely adopted in fluid dynamics
investigations over the past few years due to their ability to extract and process fluid flow …

Heavy and light particles are commonly found in many natural phenomena and industrial
processes, such as suspensions of bubbles, dust, and droplets in incompressible turbulent …