Abstract Machine learning is rapidly becoming a core technology for scientific computing,
with numerous opportunities to advance the field of computational fluid dynamics. Here we …

This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …

Modal-decomposition techniques are computational frameworks based on data aimed at
identifying a low-dimensional space for capturing dominant flow features: the so-called …

As a form of clean energy, nuclear energy has unique advantages compared to other energy
sources in the present era, where low-carbon policies are being widely advocated. The …

Accurate flow field prediction methods are needed for the analysis of complex flows in
energy and power field. Flow field and temperature field prediction methods combining …

Variational autoencoders (VAEs) have shown promising potential as artificial neural
networks (NN) for develo** reduced-order models (ROMs) in the context of turbulent …

Conducting large-scale numerical computations to obtain flow field during the hypersonic
vehicle engineering design phase can be excessively costly. Although deep learning …

Urban areas are not only one of the biggest contributors to climate change, but also they are
one of the most vulnerable areas with high populations who would together experience the …

Fluid dynamics problems are characterized by being multidimensional and nonlinear.
Therefore, experiments and numerical simulations are complex and time-consuming …

The low-rank and sparse decomposition model has been favored by the majority of
hyperspectral image anomaly detection personnel, especially the robust principal …