Accelerated Gradient Based Optimization Using Adjoint Sensitivities

Comparison of Gradient and Response Surface Based Optimization Frameworks Using Adjoint Method

This paper deals with aerodynamic shape optimization using an high fidelity solver. Due to the computational cost and restitution time needed to solve the RANS equations, this type of optimization framework must improve the solution using very few objective function evaluations despite the high number of design variables. The choice of the optimizer is thus largely based on its speed of converg...

Accelerated gradient sliding for structured convex optimization

Our main goal in this paper is to show that one can skip gradient computations for gradient descent type methods applied to certain structured convex programming (CP) problems. To this end, we first present an accelerated gradient sliding (AGS) method for minimizing the summation of two smooth convex functions with different Lipschitz constants. We show that the AGS method can skip the gradient...

Adjoint-based Shape Sensitivities for Turbomachinery Design Optimizations

An adjoint preprocess for an adjoint-based turbomachinery design process is described. The resulting process is compared to a previously established adjoint process based on three-dimensional adjoint solutions and deformed meshes. Within the new process, the calculation of sensitivities is performed using shape sensitivities and surface displacements, where the design parameters are computer-ai...

Towards Adjoint Based Constrained Optimization

Gradient-based optimization using parametric sensitivity macromodels

A new method for gradient-based optimization of electromagnetic systems using parametric sensitivity macromodels is presented. Parametric macromodels accurately describe the parameterized frequency behavior of electromagnetic systems and their corresponding parameterized sensitivity responses with respect to design parameters, such as layout and substrate parameters. A set of frequency-dependen...