A Comparison of Reification and Cokriging for Sequential Multi-Information Source Fusion

摘要

Many engineering tasks, such as optimization, analysis, model development, model calibration, and others, can potentially exploit information from many sources. These sources include numerical models, expert opinion, and experimental data. Information fusion over these sources of information has the potential to provide a more complete quantitative picture of the current state of knowledge of a given ground truth quantity of interest. This state of knowledge can be updated as new information from any given source is acquired. In this work, we compare two information fusion approaches that both seek to combine all available information to form a surrogate model of the ground truth. These are model reification and cokriging. The comparison considers several test functions as well as a real world NACA 0012 analysis. A quantity of interest is considered for each test case, as well as the derivative of the quantity of interest in some cases. Each fusion approach performs well generally, with each being superior to the other under certain conditions.