An Adaptive Fused Sampling Approach of High-Accuracy Data in the Presence of Low-Accuracy Data

In several applications, a large amount of low-accuracy (LA) data can be acquired at a small cost. However, in many situations, such LA data is not sucient for generating a high- delity model of a system. To adjust and improve the model constructed by LA data, a small sample of high-accuracy (HA) data, which is expensive to obtain, is usually fused with the LA data. Unfortunately, current techniques assume that the HA data is already collected and concentrate on fusion strategies, without providing guidelines on how to sample the HA data. This work addresses the problem of collecting HA data adaptively and sequentially so when it is integrated with the LA data a more accurate surrogate model is achieved. For this purpose, we propose an approach that takes advantage of the information provided by LA data as well as the previously selected HA data points and computes an improvement criterion over a design space to choose the next HA data point. The performance of the proposed method is evaluated, using both simulation and case studies. The results show the benets of the proposed method in generating an accurate surrogate model when compared to three other benchmarks.