Shape optimization utilizing consistent sensitivities

Implementation of sparse forward mode automatic differentiation with application to electromagnetic shape optimization

In this paper, we present the details of a simple lightweight implementation of the so-called sparse forward mode automatic differentiation (AD) in the C++programming language. Our implementation and the well-known ADOL-C tool (which utilizes taping and compression techniques) are used to compute Jacobian matrices of two nonlinear systems of equations from the MINPACK-2 test problem collection. Timings of the computations are presented and discussed. Moreover, we perform the shape sensitivity analysis of a time-harmonic Maxwell equation solver using our implementation and the tapeless mode of ADOL-C, which implements the dense forward mode AD. It is shown that the use of the sparse forward …

Shape optimization of systems governed by Bernoulli free boundary problems

3D-verkon generointi hybridimenetelmällä