Metamodeling techniques for CPU-intensive simulation-based design optimization: a survey

In design optimization of complex systems, the surrogate model approach relying on progressively enriched Design of Experiments (DOE) avoids efficiency problems encountered when embedding simulation codes within optimization loops. However, an efficient a priori sampling of the design space rapidly becomes costly when using High-Fidelity (HF) simulators, especially in high dimension. On the other hand, in applications such as aeronautical design, multiple simulation tools are frequently available for the same problem, generally with a degree of precision inversely proportional to the CPU cost. Thus, the concept of multi-fidelity proposes to merge different levels of fidelity within a single model with controlled variance. Based on recent Reduced-Order Modeling (ROM) techniques, an alternative approach allows to pursue the objective of mastering the simulation budget by replacing costly models with their approximate full-field counterparts, providing additional insight to scalar surrogates built directly from the Quantities of Interest (QoI). Both approaches: multi-fidelity and ROM, may be combined, allowing for additional flexibility in choosing the degree of fidelity required in different zones of the design space. This paper reviews the strategies that seek to improve surrogate-based optimization efficiency, including ROM, multi-fidelity metamodeling, and DOE enrichment strategies.