A balanced sequential design strategy for global surrogate modeling

The sequential design methodology for global surrogate modeling of complex systems consists of iteratively training the model on a growing set of samples. Sample selection is a critical step in the process and influences the final quality of the model. It is desirable to use as few samples as possible while building an accurate model using insight gained in previous iterations. A robust sampling scheme is considered that employs Monte Carlo Voronoi tessellations for exploration, linear gradients for exploitation and different schemes are investigated to balance their trade-off. The experimental results on benchmark examples indicate that some schemes can result in a substantially smaller model error especially when the system under consideration has a highly non-linear behavior.