conference paper Augustina Chidinma Amakor author 97916 Manuel B. Berkemeier author Meike Claudia Wohlleben author 439910009-0009-9767-7168 Walter Sextro author 21220 Sebastian Peitz author 474270000-0002-3389-793X WalterSenn editor MarcelloSanguineti editor AusraSaudargiene editor Igor V.Tetko editor Alessandro E. P.Villa editor ViktorJirsa editor YoshuaBengio editor 655 department 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. Springer Nature Switzerland2026 eng ownown-conference 978-3-032-04555-310.1007/978-3-032-04555-3_21 251–262 @inproceedings{Amakor_Berkemeier_Wohlleben_Sextro_Peitz_2026, place={Cham}, title={Surrogate-Assisted Multi-objective Design of Complex Multibody Systems}, DOI={<a href="https://doi.org/10.1007/978-3-032-04555-3_21">10.1007/978-3-032-04555-3_21</a>}, booktitle={Artificial Neural Networks and Machine Learning – ICANN 2025}, publisher={Springer Nature Switzerland}, author={Amakor, Augustina Chidinma and Berkemeier, Manuel B. and Wohlleben, Meike Claudia and Sextro, Walter and Peitz, Sebastian}, editor={Senn, Walter and Sanguineti, Marcello and Saudargiene, Ausra and Tetko, Igor V. and Villa, Alessandro E. P. and Jirsa, Viktor and Bengio, Yoshua}, year={2026}, pages={251–262} } Amakor AC, Berkemeier MB, Wohlleben MC, Sextro W, Peitz S. Surrogate-Assisted Multi-objective Design of Complex Multibody Systems. In: Senn W, Sanguineti M, Saudargiene A, et al., eds. <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i>. Springer Nature Switzerland; 2026:251–262. doi:<a href="https://doi.org/10.1007/978-3-032-04555-3_21">10.1007/978-3-032-04555-3_21</a> Amakor, Augustina Chidinma, et al. “Surrogate-Assisted Multi-Objective Design of Complex Multibody Systems.” <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i>, edited by Walter Senn et al., Springer Nature Switzerland, 2026, pp. 251–262, doi:<a href="https://doi.org/10.1007/978-3-032-04555-3_21">10.1007/978-3-032-04555-3_21</a>. A.C. Amakor, M.B. Berkemeier, M.C. Wohlleben, W. Sextro, S. Peitz, in: W. Senn, M. Sanguineti, A. Saudargiene, I.V. Tetko, A.E.P. Villa, V. Jirsa, Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025, Springer Nature Switzerland, Cham, 2026, pp. 251–262. Amakor, Augustina Chidinma, Manuel B. Berkemeier, Meike Claudia Wohlleben, Walter Sextro, and Sebastian Peitz. “Surrogate-Assisted Multi-Objective Design of Complex Multibody Systems.” In <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i>, edited by Walter Senn, Marcello Sanguineti, Ausra Saudargiene, Igor V. Tetko, Alessandro E. P. Villa, Viktor Jirsa, and Yoshua Bengio, 251–262. Cham: Springer Nature Switzerland, 2026. <a href="https://doi.org/10.1007/978-3-032-04555-3_21">https://doi.org/10.1007/978-3-032-04555-3_21</a>. A. C. Amakor, M. B. Berkemeier, M. C. Wohlleben, W. Sextro, and S. Peitz, “Surrogate-Assisted Multi-objective Design of Complex Multibody Systems,” in <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i>, 2026, pp. 251–262, doi: <a href="https://doi.org/10.1007/978-3-032-04555-3_21">10.1007/978-3-032-04555-3_21</a>. Amakor, A. C., Berkemeier, M. B., Wohlleben, M. C., Sextro, W., &#38; Peitz, S. (2026). Surrogate-Assisted Multi-objective Design of Complex Multibody Systems. In W. Senn, M. Sanguineti, A. Saudargiene, I. V. Tetko, A. E. P. Villa, V. Jirsa, &#38; Y. Bengio (Eds.), <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i> (pp. 251–262). Springer Nature Switzerland. <a href="https://doi.org/10.1007/978-3-032-04555-3_21">https://doi.org/10.1007/978-3-032-04555-3_21</a> 673082026-10-01T12:02:40Z2026-10-01T12:03:16Z