<ul><li>The optimization of large-scale multibody systems with multiple conflicting criteria is a challenging task.</li><li>Surrogate models, constructed from a small but informative number of expensive model evaluations, are commonly used to approximate the Pareto set of optimal compromises.</li><li>A back-and-forth approach between surrogate modeling and multi-objective optimization is presented to improve solution quality.</li><li>Different strategies regarding multi-objective optimization, sampling, and surrogate modeling are compared to determine the most efficient and high-quality approach.</li></ul>

Surrogate-assisted multi-objective design of complex multibody systems

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