Mattes, Michael (Laboratoire d’Electromagnétisme et d’Acoustique, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland)
This communication addresses the problem of any simulation tool: the accurate and efficient sampling of a physical observable with respect to a parameter. A popular sampling technique is the uniform sampling combined with a straight-line interpolation for representing the continuous variation of the observable. However, this sampling becomes rapidly inefficient if the observable varies strongly since a high-oversampling is necessary due to Nyquist’s theorem. An alternative is nonlinear sampling and nonlinear interpolation of the sampling points. Another reason why more efficient sampling techniques are needed is the optimization of devices using full-wave simulation tools where the reduction of sampling points is essential to accelerate the design of a component. This paper presents an algorithm that is based on idea’s of the model-based parameter estimation (MBPE) and the Genetic Algorithm (GA).