Switched reluctance machines (SRMs) have recently attracted more interest in many
applications due to the volatile prices of rare-earth permanent magnets (PMs) used in …

Automatic differentiation is a popular technique for computing derivatives of computer
programs. While automatic differentiation has been successfully used in countless …

ADJOINT methods are widely used to compute gradients for numerical optimization
problems that involve the simulation of complex physical phenomena, such as fluid flow [1 …

Automatic differentiation, also known as backpropagation, AD, autodiff, or algorithmic
differentiation, is a popular technique for computing derivatives of computer programs …

The focus of this thesis lies on the efficient generation of sensitivity information for
computational fluid dynamics (CFD) applications, using the discrete adjoint approach …

In the context of numerical simulation a system of partial differential equations is typically
solved by discretizing it into a set of linear equations. The non-linear effects in the solution …

The first part of this thesis presents the advances made in the Open-Source software SU2,
towards transforming it into a high-performance framework for design and optimization of …

Simulations are used in science and industry to predict the performance of technical
systems. Adjoint derivatives of these simulations can reveal the sensitivity of the system …

Multiphysics software needs derivatives for, eg, solving a system of non-linear equations,
conducting model verification, or sensitivity studies. In C++, algorithmic differentiation (AD) …

Previous papers have shown the impact of partial convergence of discretized PDEs on the
accuracy of tangent and adjoint linearizations. A series of papers suggested linearization of …