... selection within the parameter space is paramount. Numerous design of experiments (DOE) methodologies have been developed with the aim of identifying the optimal sample set to capture the system of interest. Adaptive sampling techniques are a subclass of DOE methods that identify optimal locations for new...

... uncertainty in MCS. Uncertainty change is estimated by adding a virtual sample from the current GP and calculating the change in CV, which is called expected uncertainty change (EUC). The proposed method can help adaptive sampling schemes to determine when to stop before adding a sample. In numerical examples...

...Anton van Beek; Siyu Tao; Matthew Plumlee; Daniel W. Apley; Wei Chen The cost of adaptive sampling for global metamodeling depends on the total number of costly function evaluations and to which degree these evaluations are performed in parallel. Conventionally, samples are taken through a greedy...

...Yiming Zhang; Nam H. Kim; Raphael T. Haftka A surrogate model is a common tool to approximate system response at untested points for design space exploration. Adaptive sampling has been studied for improving the accuracy of surrogates iteratively by introducing additional samples (simulations...