Although deep learning has achieved remarkable success in various scientific machine
learning applications, its opaque nature poses concerns regarding interpretability and …

The prediction of turbulent flow by the application of machine learning (ML) algorithms to big
data is a concept currently in its infancy which requires further development. It is of special …

Conventional aerodynamic inverse design (AID) methods have major limitations in terms of
optimality and actuality of target parameter distribution. In this research, the target pressure …

Turbomachinery plays a vital role in energy conversion systems, with aerodynamic issues
being integral to its entire lifecycle, spanning the period of design, validation, and …

Data-driven modeling has contributed significantly to the field of computational fluid
dynamics (CFD), but integrating machine-learning (ML) models into a CFD workflow still …

A neural-networks predictor library has been developed to deploy machine learning (ML)
models into computational fluid dynamics (CFD) codes. The pointer-to-implementation …

Aerodynamic design exploration is pivotal in turbomachinery design, enabling engineers to
optimize performance and uncover solutions that enhance efficiency. To aid this endeavour …