Extracting optimal datasets for metamodelling and perspectives for incremental samplings

摘要

Selecting the best input values for the purpose of fitting a metamodel to the response of a computer code presents several issues. Classical designs for physical experiments (DoE) have been developed to deal with noisy responses, while general space filling designs, though being usually effective for complete classes of problems, are not easily translated into adaptive incremental designs for specific problems. We discuss one-stage and incremental strategies for generating designs of experiments encountered in literature and present an extraction technique along with some benchmark on theoretical functions. We finally propose complexity indicators which could be considered for developing effective incremental samplings.