@inproceedings{67308,
  abstract     = {{Optimizing large-scale multibody systems is a challenging task, particularly in the presence of multiple conflicting criteria. To prevent high simulation costs, surrogate models constructed from a small number of expensive model evaluations are very popular. However, it is difficult to ensure the optimality of the obtained solutions using a single pre-computed model. We present a back-and-forth approach between surrogate modeling and multi-objective optimization, and we compare different strategies for optimization, sampling, and surrogate modeling, to identify the most promising approach in terms of computational efficiency and solution quality.}},
  author       = {{Amakor, Augustina Chidinma and Berkemeier, Manuel B. and Wohlleben, Meike Claudia and Sextro, Walter and Peitz, Sebastian}},
  booktitle    = {{Artificial Neural Networks and Machine Learning – ICANN 2025}},
  editor       = {{Senn, Walter and Sanguineti, Marcello and Saudargiene, Ausra and Tetko, Igor V. and Villa, Alessandro E. P. and Jirsa, Viktor and Bengio, Yoshua}},
  isbn         = {{978-3-032-04555-3}},
  keywords     = {{own, own-conference}},
  pages        = {{251–262}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Surrogate-Assisted Multi-objective Design of Complex Multibody Systems}}},
  doi          = {{10.1007/978-3-032-04555-3_21}},
  year         = {{2026}},
}