Magalhães, H.Marques, F.Liu, B.Pombo, JoãoFlores, P.Ambrósio, JorgeBruni, S.2018-10-112018-10-112018-09-14MAGALHÃES, H; [et al] – An optimization approach to generate accurate and efficient lookup tables for engineering applications. EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization. ISBN 978-3-319-97772-0. (2018), pp. 1446-1457978-3-319-97772-0978-3-319-97773-7http://hdl.handle.net/10400.21/8913In a wide number of engineering applications, the interpolation of a lookup table (LUT) can substitute expensive calculations. The generation of a LUT consists of pre-calculating a set of quantities from a collection of points that covers the study domain where the interpolation is possible. Nevertheless, the selection of points where the exact calculation is performed is of utmost im-portance for the LUT size and accuracy. Thus, the goal of this paper is to provide a dedicated optimization tool for the generation of accurate and efficient LUT. Here, the domain of the LUT is structured by the so-called layers, in which, the thicknesses of each layer define the distance between pre-calculated points. The optimization problem consists of maximizing the layer thicknesses, that is, min-imizing the LUT size, such that the interpolation errors within the layer domain are kept under specified tolerances. Thus, a sequential design approach is applied to design each layer of the LUT until the layers cover the study domain. To achieve reliable LUT generations, a new optimization algorithm has been imple-mented to reach optimal layers with minimum iterations. The strategy proposed here is applied to generate a LUT to substitute a selected analytical function. The optimization procedures demonstrate not only the performance of the optimiza-tion algorithm, but also its convenience in the generation of multidimensional LUT.engLookup table generationSequential optimal designDiagonal searchAn optimization approach to generate accurate and efficient lookup tables for engineering applicationsconference objecthttps://doi.org/10.1007/978-3-319-97773-7_124