Surrogate modeling techniques are essential for costly design assessments. Recent studies focus on characterizing harmful data sources to guide model construction. Instance Space Analysis provides insights into when to rely on low-fidelity sources. SVM predictions aid in algorithm selection based on performance analysis.
Surrogate models like Kriging and Co-Kriging are widely used for multi-fidelity problems. The study compares their accuracy using benchmark filtering techniques. The research aims to provide guidelines for practitioners in industrial settings.
The study uses a diverse set of function pairs, including literature-based and disturbance-based instances, along with simulations from the SOLAR engine. SVMs predict the performance of Kriging and Co-Kriging models based on selected features.
Analysis reveals that relative sample budgets play a significant role in model performance prediction. Features related to high-fidelity data availability show strong correlations with algorithm performance. Instance Space Analysis aids in understanding when to utilize low-fidelity sources effectively.
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