This paper delves into the critical role of Navier-Stokes equations in biomedical engineering,
with a specific focus on their application in the development of medical devices across the …

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

The computational prediction of wave propagation in dam-break floods is a long-standing
problem in hydrodynamics and hydrology. We show that a reservoir computing echo state …

This paper concerns analyses of virus droplet dynamics resulting from coughing events
within a confined environment using, as an example, a typical cruiser's cabin. It is of …

This paper concerns an investigation of the invariance and consistency of deep learning of
turbulent pressure fluctuations. The long-shortmemory model is employed to predict wall …

Data science and machine learning (ML) techniques are employed to shed light into the
molecular mechanisms that affect fluid-transport properties at the nanoscale. Viscosity and …

Deep learning has been going through rapid advancement and becoming useful in scientific
computation, with many opportunities to be applied to various fields, including but not limited …

The success of deep learning models in fluid dynamics applications will depend on their
ability to handle sparse and noisy data accurately. This paper concerns the development of …

In the face of mounting economic constraints, researchers are increasingly turning to data-
driven methods for reconstructing unknown global fields from limited data. While traditional …

Purpose The continuous evolution of SARS-CoV-2 and possible future pandemics have
risen concerns relevant to the effectiveness of the vaccines which are currently available. To …