Final thoughts are given on opportunities for future research. plot the evolution of the objective function, or compare the distribution of attributes in the initial object and in the sampled subset, you need to switch the simple option to FALSE. The points are to be chosen in such a way that no two points have any coordinate value in common. If you want to report on the cLHS results, e.g.
Each of the M coordinate dimensions is discretized to the values 1 through N. N Points in an M dimensional Latin hypercube are to be selected. The discussion starts with the early developments in optimization of the point selection and goes all the way to the pitfalls of the indiscriminate use of Latin hypercube designs. IHS, a MATLAB library which carries out the Improved Hypercube Sampling (IHS) algorithm. This paper provides a tutorial on Latin hypercube design of experiments, highlighting potential reasons of its widespread use. Among the strategies devised for computer experiments, Latin hypercube designs have become particularly popular.
The first step for a successful surrogate modeling and statistical analysis is the planning of the input configuration that is used to exercise the simulation code. Generally, when simulations are time consuming, a surrogate model replaces the computer code in further studies (e.g., optimization, sensitivity analysis, etc.). Those samples are simulated in analytical buckling load, developed in Matlab code, to generate the training support vector machine. The growing power of computers enabled techniques created for design and analysis of simulations to be applied to a large spectrum of problems and to reach high level of acceptance among practitioners.
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