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

Antony Jameson pioneered CFD-based aerodynamic design optimization in the late 1980s.
In addition to develo** the fundamental theory, Jameson implemented that theory in …

The adjoint method is used for high-fidelity aerodynamic shape optimization and is an
efficient approach for computing the derivatives of a function of interest with respect to a …

Computational fluid dynamics through the solution of the Navier–Stokes equations with
turbulence models has become commonplace. However, simply solving these equations is …

Despite considerable research on aerodynamic shape optimization, there is no standard
benchmark problem allowing researchers to compare results. This work addresses this …

The global optimization of a high-dimensional black-box function under black-box
constraints is a pervasive task in machine learning, control, and engineering. These …

Accelerator physics relies on numerical algorithms to solve optimization problems in online
accelerator control and tasks such as experimental design and model calibration in …

The favorable convergence rates of Newton–Krylov-based solution algorithms have
increased their popularity for computational fluid dynamics applications. Unfortunately, these …

Surrogate models provide an affordable alternative to the evaluation of expensive
deterministic functions. However, the construction of accurate surrogate models with many …

Since its introduction, the NASA Common Research Model has proved a useful
aerodynamic benchmark for predicting computational-fluid-dynamics-based drag and …