[{"page":"251–262","_id":"67308","publisher":"Springer Nature Switzerland","user_id":"47427","editor":[{"full_name":"Senn, Walter","last_name":"Senn","first_name":"Walter"},{"full_name":"Sanguineti, Marcello","first_name":"Marcello","last_name":"Sanguineti"},{"full_name":"Saudargiene, Ausra","first_name":"Ausra","last_name":"Saudargiene"},{"full_name":"Tetko, Igor V.","first_name":"Igor V.","last_name":"Tetko"},{"last_name":"Villa","first_name":"Alessandro E. P.","full_name":"Villa, Alessandro E. P."},{"full_name":"Jirsa, Viktor","first_name":"Viktor","last_name":"Jirsa"},{"first_name":"Yoshua","last_name":"Bengio","full_name":"Bengio, Yoshua"}],"status":"public","place":"Cham","citation":{"apa":"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>","ieee":"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>.","short":"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.","chicago":"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>.","mla":"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>.","ama":"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>","bibtex":"@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} }"},"language":[{"iso":"eng"}],"doi":"10.1007/978-3-032-04555-3_21","year":"2026","title":"Surrogate-Assisted Multi-objective Design of Complex Multibody Systems","publication_identifier":{"isbn":["978-3-032-04555-3"]},"author":[{"first_name":"Augustina Chidinma","last_name":"Amakor","full_name":"Amakor, Augustina Chidinma","id":"97916"},{"full_name":"Berkemeier, Manuel B.","first_name":"Manuel B.","last_name":"Berkemeier"},{"first_name":"Meike Claudia","orcid":"0009-0009-9767-7168","last_name":"Wohlleben","full_name":"Wohlleben, Meike Claudia","id":"43991"},{"id":"21220","last_name":"Sextro","first_name":"Walter","full_name":"Sextro, Walter"},{"id":"47427","last_name":"Peitz","orcid":"0000-0002-3389-793X","first_name":"Sebastian","full_name":"Peitz, Sebastian"}],"date_updated":"2026-10-01T12:03:16Z","date_created":"2026-10-01T12:02:40Z","keyword":["own","own-conference"],"type":"conference","department":[{"_id":"655"}],"publication":"Artificial Neural Networks and Machine Learning – ICANN 2025","abstract":[{"lang":"eng","text":"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."}]}]