Multi-fidelity error-estimate-based model management

This paper presents a novel multi-fidelity model-management framework based on the estimated error between the low-fidelity and high-fidelity models. The optimization algorithm is similar to classical multi-fidelity trust-region model-management approaches, but it replaces the trust-radius constraint with a bound on the estimated error between the low- and high-fidelity models. This enables globalization without requiring the user to specify non-intuitive parameters such as the initial trust radius, which have a significant impact on the cost of the optimization yet can be hard to determine a priori. We demonstrate the framework on a simple one-dimensional optimization problem, a series of analytical benchmark problems, and a realistic electric-motor optimization. We show that for low-fidelity models that accurately capture the trends of the high-fidelity model, the developed framework can significantly improve the efficiency of obtaining high-fidelity optima compared to state-of-the-art multi-fidelity optimization methods and a direct high-fidelity optimization.