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Efficient strategies for surrogate-based optimization including multifidelity and reduced-order models

One of the main difficulties of optimization is the computational cost associated with the evaluation of numerical models. In order to overcome this problem, many works have emerged in the last few years based on model reduction methods, surrogate model and multifidelity and their coupling. This special issue focuses on these growing tools.

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