Editorial—surrogate modeling and space mapping for engineering optimization

Advances in optimization technology, a cornerstone in engineering modeling, simulation-based design and manufacturing, continue to push back the boundaries of feasibility. Multi-disciplinary optimization continues to show success. Notwithstanding advances in computing power and user-friendly management of multidisciplinary software, challenging problems will undoubtedly continue to plague the designer as long as engineering projects grow in ambition. Ever more efficient and systematic procedures are proposed that exploit surrogate or approximate models with occasional reference to appropriate computationally intensive high-fidelity simulator(s). Such low-fidelity models facilitate rapid optimization. Data interpolation techniques continue their development, including artificial neural network approaches, kriging, and low-order response surfaces. Space mapping, where a cheap (low-fidelity or coarse) physics-based model provides an effective optimization surrogate for a more detailed or high-fidelity model, has made significant inroads into the surrogate modeling field. A crucial property for traditional optimization is that each iteration towards the solution focus on a single