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 du e to Nyquist's theorem. An alternative is nonlinear sampling and nonlinear interpolation of the sampling points. Another reaso n 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 ba sed on ideas coming from the model-based parameter estimation (MBPE) and the Genetic Algorithm (GA).
Edouard Robert Albert Patrick Dufour, Colin Pelletier, Pascal Fua, Zhen Wei
François Maréchal, Jonas Schnidrig, Cédric Terrier, Arthur Antoine Chuat
Lenka Zdeborová, Emanuele Troiani, Giovanni Piccioli