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