Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

Full scale aerodynamic wind tunnel testing, numerical simulation of high dimensional (full-
order) aerodynamic models or flight testing are some of the fundamental but complex steps …

Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and
noisy, observations must be effectively combined with limited or expensive true data in order …

To achieve multi-response prediction and reliability evaluation of complex structural system,
a high efficient and precision strategy, namely synchronous surrogate modeling-based …

This paper presents a novel infill-sampling strategy for adaptive gradient-enhanced Kriging
(AGEK) that delivers superior results on a limited budget. The primary innovation of this …

Metamodeling, the science of modeling functions observed at a finite number of points,
benefits from all auxiliary information it can account for. Function gradients are a common …

Partial differential equations (PDEs) are important tools to model physical systems and
including them into machine learning models is an important way of incorporating physical …

Data in many applications follows systems of Ordinary Differential Equations (ODEs). This
paper presents a novel algorithmic and symbolic construction for covariance functions of …

Metamodelling or surrogate modelling techniques are frequently used across the
engineering disciplines in conjunction with expensive simulation models or physical …

This paper explores the use of Gradient-enhanced Kriging for optimization of expensive
black-box design problems, which is not completely limited by the conventional Efficient …