Therefore a cheap surrogate model is used to capture the main trends of the objective and constraint functions.
To cope with the cost of simulations, one replaces the real response surface with a cheap surrogate based, e.g., on polynomial chaos expansions, neural networks, support vector machines, or Gaussian processes (GP).
The solution approaches that we present here rely on surrogate-based optimization (SBO) paradigm, where the design speedup is obtained by shifting the optimization burden into a cheap replacement model (the surrogate).
In SBO, the direct optimization of the expensive model is replaced by iterative updating and re-optimization of its cheap surrogate model.
In place of this we are offered a cheap and extremely dangerous surrogate for the internal human disposition toward change: political movements of one sort or another.
By shifting the optimization burden to a cheap and yet reasonably accurate surrogate model, the design cost can be substantially reduced.
In order to address the problem of the curse of dimensionality, this work proposes essentially variance-based adaptive strategies aiming to build a cheap meta-model (i.e. surrogate model) by employing the sparse PDD approach with its coefficients computed by regression.
The results show that the surrogate modelling approach provides a cheap and yet efficient method for systematically investigating the effect of different parameters on the performance of the plasma actuator.
