Surrogate-Assisted Multi-objective Design of Complex Multibody Systems Amakor, Augustina Chidinma Berkemeier, Manuel B. Wohlleben, Meike Claudia Sextro, Walter Peitz, Sebastian Senn, Walter Sanguineti, Marcello Saudargiene, Ausra Tetko, Igor V. Villa, Alessandro E. P. Jirsa, Viktor Bengio, Yoshua own own-conference 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 Switzerland 2026 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://ris.uni-paderborn.de/record/67308 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> eng info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-032-04555-3_21 info:eu-repo/semantics/altIdentifier/isbn/978-3-032-04555-3 info:eu-repo/semantics/closedAccess