Surrogate-Assisted Multi-objective Design of Complex Multibody Systems
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.
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Amakor, Augustina ChidinmaLibreCat;
Berkemeier, Manuel B.;
Wohlleben, Meike ClaudiaLibreCat 
;
Sextro, WalterLibreCat;
Peitz, SebastianLibreCat
;
Sextro, WalterLibreCat;
Peitz, SebastianLibreCat Editor
Senn, Walter;
Sanguineti, Marcello;
Saudargiene, Ausra;
Tetko, Igor V.;
Villa, Alessandro E. P.;
Jirsa, Viktor;
Bengio, Yoshua
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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.
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Proceedings Title
Artificial Neural Networks and Machine Learning – ICANN 2025
Page
251–262
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Cite this
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. Artificial Neural Networks and Machine Learning – ICANN 2025. Springer Nature Switzerland; 2026:251–262. doi:10.1007/978-3-032-04555-3_21
Amakor, A. C., Berkemeier, M. B., Wohlleben, M. C., Sextro, W., & 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, & Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025 (pp. 251–262). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-04555-3_21
@inproceedings{Amakor_Berkemeier_Wohlleben_Sextro_Peitz_2026, place={Cham}, title={Surrogate-Assisted Multi-objective Design of Complex Multibody Systems}, DOI={10.1007/978-3-032-04555-3_21}, 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, Augustina Chidinma, Manuel B. Berkemeier, Meike Claudia Wohlleben, Walter Sextro, and Sebastian Peitz. “Surrogate-Assisted Multi-Objective Design of Complex Multibody Systems.” In Artificial Neural Networks and Machine Learning – ICANN 2025, 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. https://doi.org/10.1007/978-3-032-04555-3_21.
A. C. Amakor, M. B. Berkemeier, M. C. Wohlleben, W. Sextro, and S. Peitz, “Surrogate-Assisted Multi-objective Design of Complex Multibody Systems,” in Artificial Neural Networks and Machine Learning – ICANN 2025, 2026, pp. 251–262, doi: 10.1007/978-3-032-04555-3_21.
Amakor, Augustina Chidinma, et al. “Surrogate-Assisted Multi-Objective Design of Complex Multibody Systems.” Artificial Neural Networks and Machine Learning – ICANN 2025, edited by Walter Senn et al., Springer Nature Switzerland, 2026, pp. 251–262, doi:10.1007/978-3-032-04555-3_21.
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