Abstract. In engineering design the use of approximation models ( = surrogate models) has become standard practice for design space exploration, sensitivity analysis, visualization and optimization. Popular surrogate model types include neural networks, support vector machines, Kriging models, and splines. An engi-neering simulation typically involves multiple response variables that must be ap-proximated. With many approximation methods available, the question of which method to use for which response consistently arises among engineers and do-main experts. Traditionally, the different responses are modeled separately by in-dependent models, possibly involving a comparison among model types. Instead, this paper proposes a multi-objective approach can benefit the domain expert since it enables automatic model type selection for each output on the fly with-out resorting to multiple runs. In effect the optimal model complexity and model type for each output is determined automatically. In addition a multi-objective ap-proach gives information about output correlation and facilitates the generation of diverse ensembles. The merit of this approach is illustrated with a modeling problem from aerospace. 1

Abstract. When dealing with computationally expensive simulation codes or process measurement data, global surrogate modeling methods are firmly estab-lished as facilitators for design space exploration, sensitivity analysis, visualiza-tion and optimization. Popular surrogate model types include neural networks, support vector machines, and splines. In addition, the cost of each simulation mandates the use of active learning strategies where data points (simulations) are selected intelligently and incrementally. When applying surrogate models to multi-output systems, the hyperparameter optimization problem is typically for-mulated in a single objective way. The different response outputs are modeled separately by independent models. Instead, a multi-objective approach would benefit the domain expert by giving information about output correlation, facili-tate the generation of diverse ensembles, and enable automatic model type selec-tion for each output on the fly. This paper outlines a multi-objective approach to surrogate model generation including its application to two problems. 1