---
_id: '67294'
abstract:
- lang: eng
text: "ABSTRACT\r\n Hybrid modeling
aims to combine physical and data‐driven models to increase simulation accuracy
without losing physical interpretability. In the context of dynamic mechanical
systems, this enables the compensation of modeling inaccuracies that arise from
simplifications, missing effects, or uncertain parameters. In this work, a hybrid
model is used as a starting point, in which the discrepancy between simulation
and measurement is learned and compensated by a data‐driven correction element.
To integrate such models into commercial multibody simulation software like Adams
or Simpack, the formulation is adapted to operate directly on the force level.
This allows implementation via standard co‐simulation interfaces without modifying
the system's differential equations or solvers. The method is demonstrated using
a three‐mass oscillator with synthetic measurement data. Results show that the
coupled simulation works reliably and that the hybrid model significantly improves
accuracy while remaining compatible with established industrial simulation workflows."
article_number: e70215
author:
- first_name: Meike Claudia
full_name: Wohlleben, Meike Claudia
id: '43991'
last_name: Wohlleben
orcid: 0009-0009-9767-7168
- first_name: Jill Mercedes
full_name: Linneweber, Jill Mercedes
id: '57639'
last_name: Linneweber
orcid: https://orcid.org/0009-0002-1910-358X
- first_name: Jan
full_name: Schütte, Jan
id: '22109'
last_name: Schütte
orcid: 0000-0001-9025-9742
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Wohlleben MC, Linneweber JM, Schütte J, Sextro W. Enabling Hybrid Modeling
in Commercial MBS Software: A Force‐Level Approach. Proceedings in Applied
Mathematics and Mechanics. 2026;26(4). doi:10.1002/pamm.70215'
apa: 'Wohlleben, M. C., Linneweber, J. M., Schütte, J., & Sextro, W. (2026).
Enabling Hybrid Modeling in Commercial MBS Software: A Force‐Level Approach. Proceedings
in Applied Mathematics and Mechanics, 26(4), Article e70215. https://doi.org/10.1002/pamm.70215'
bibtex: '@article{Wohlleben_Linneweber_Schütte_Sextro_2026, title={Enabling Hybrid
Modeling in Commercial MBS Software: A Force‐Level Approach}, volume={26}, DOI={10.1002/pamm.70215}, number={4e70215},
journal={Proceedings in Applied Mathematics and Mechanics}, publisher={Wiley},
author={Wohlleben, Meike Claudia and Linneweber, Jill Mercedes and Schütte, Jan
and Sextro, Walter}, year={2026} }'
chicago: 'Wohlleben, Meike Claudia, Jill Mercedes Linneweber, Jan Schütte, and Walter
Sextro. “Enabling Hybrid Modeling in Commercial MBS Software: A Force‐Level Approach.”
Proceedings in Applied Mathematics and Mechanics 26, no. 4 (2026). https://doi.org/10.1002/pamm.70215.'
ieee: 'M. C. Wohlleben, J. M. Linneweber, J. Schütte, and W. Sextro, “Enabling Hybrid
Modeling in Commercial MBS Software: A Force‐Level Approach,” Proceedings in
Applied Mathematics and Mechanics, vol. 26, no. 4, Art. no. e70215, 2026,
doi: 10.1002/pamm.70215.'
mla: 'Wohlleben, Meike Claudia, et al. “Enabling Hybrid Modeling in Commercial MBS
Software: A Force‐Level Approach.” Proceedings in Applied Mathematics and Mechanics,
vol. 26, no. 4, e70215, Wiley, 2026, doi:10.1002/pamm.70215.'
short: M.C. Wohlleben, J.M. Linneweber, J. Schütte, W. Sextro, Proceedings in Applied
Mathematics and Mechanics 26 (2026).
date_created: 2026-10-01T08:31:35Z
date_updated: 2026-10-01T09:06:26Z
department:
- _id: '151'
doi: 10.1002/pamm.70215
intvolume: ' 26'
issue: '4'
language:
- iso: eng
publication: Proceedings in Applied Mathematics and Mechanics
publication_identifier:
issn:
- 1617-7061
- 1617-7061
publication_status: published
publisher: Wiley
status: public
title: 'Enabling Hybrid Modeling in Commercial MBS Software: A Force‐Level Approach'
type: journal_article
user_id: '43991'
volume: 26
year: '2026'
...
---
_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. Artificial Neural Networks and Machine Learning
– ICANN 2025. Springer Nature Switzerland; 2026:251–262. doi:10.1007/978-3-032-04555-3_21'
apa: 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
bibtex: '@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}
}'
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 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.'
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 Artificial
Neural Networks and Machine Learning – ICANN 2025, 2026, pp. 251–262, doi:
10.1007/978-3-032-04555-3_21.'
mla: 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.
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'
...
---
_id: '67419'
abstract:
- lang: eng
text: Die Modellierung der Dynamik technischer Systeme bewegt sich zwischen physikalisch
fundierten White-Box-Ansätzen und rein datengetriebenen Black-Box-Modellen. Gray-Box-Modelle
verbinden beide Paradigmen. Hybride Modelle stellen dabei einen Sonderfall dar,
bei dem physikalisches Wissen explizit beibehalten und gezielt um datenbasierte
Anteile ergänzt wird. Diese Arbeit untersucht hybride Modellierungsansätze auf
Basis von Diskrepanzen zur Kompensation von Modellabweichungen. Obwohl dazu verschiedene
Ansätze in der Literatur vorgeschlagen wurden, fehlt bislang ein systematischer
Vergleich unter identischen Bedingungen. Auf Systemebene werden kontinuierliche
und diskrete Verfahren für vollständig sowie nicht vollständig beobachtbare Systeme
analysiert, sodass insgesamt fünf Ansätze gegenübergestellt werden. Die Bewertung
erfolgt anhand synthetischer Messdaten eines Drei-Massen-Schwingers, die reale
Bedingungen wie Rauschen, Trends und vereinfachte Modellannahmen isoliert berücksichtigen
und damit Aussagen zur Generalisierungsfähigkeit erlauben. Anschließend werden
ausgewählte Verfahren auf reale Messdaten eines Gelenkvierecks und eines Doppelpendels
angewendet. Der Vergleich zeigt, dass kein Verfahren universell überlegen ist,
sondern die Eignung maßgeblich von Anwendungskontext, Messsituation und verfügbarer
Systembeschreibung abhängt. Die Übertragung auf Komponentenebene wird an Stoßdämpfer
und Gummi-Metall-Lager demonstriert. Es zeigt sich, dass hybride Modellierung
die Rechenzeit bei gleichbleibender Genauigkeit deutlich reduziert.
- lang: eng
text: The modeling of the dynamics of technical systems ranges between physics-based
white-box approaches and purely data-driven black-box approaches. Gray-box models
combine both approaches. Hybrid models represent a special case in which physical
knowledge is explicitly retained and specifically supplemented by data-driven
components.This work investigates hybrid modeling approaches based on discrepancies
for the compensation of model deviations. Although various approaches have been
proposed in the literature, a systematic comparison under identical conditions
is still lacking. At the system level, continuous and discrete methods for fully
and partially observable systems are analyzed, resulting in a comparison of five
approaches in total. The evaluation is carried out using synthetic measurement
data from a three-mass oscillator, which incorporates real conditions such as
noise, trends, and simplified model assumptions in isolation, thereby enabling
statements on generalization capability. Subsequently, selected methods are applied
to real measurement data from a four-bar linkage and a double pendulum.The comparison
shows that no method is universally superior, but rather that suitability depends
on application context, measurement situation, and available system description.The
transfer to component level is demonstrated using shock absorbers and rubber-metal
bushings. It is shown that hybrid modeling significantly reduces computation time
while maintaining accuracy.
author:
- first_name: Meike
full_name: Wohlleben, Meike
id: '43991'
last_name: Wohlleben
orcid: 0009-0009-9767-7168
citation:
ama: Wohlleben M. Hybride Modellierung Der Dynamik Technischer Systeme Auf Basis
von Diskrepanzen. LibreCat University; 2026. doi:10.17619/UNIPB/1-2752
apa: Wohlleben, M. (2026). Hybride Modellierung der Dynamik technischer Systeme
auf Basis von Diskrepanzen. LibreCat University. https://doi.org/10.17619/UNIPB/1-2752
bibtex: '@book{Wohlleben_2026, title={Hybride Modellierung der Dynamik technischer
Systeme auf Basis von Diskrepanzen}, DOI={10.17619/UNIPB/1-2752},
publisher={LibreCat University}, author={Wohlleben, Meike}, year={2026} }'
chicago: Wohlleben, Meike. Hybride Modellierung Der Dynamik Technischer Systeme
Auf Basis von Diskrepanzen. LibreCat University, 2026. https://doi.org/10.17619/UNIPB/1-2752.
ieee: M. Wohlleben, Hybride Modellierung der Dynamik technischer Systeme auf
Basis von Diskrepanzen. LibreCat University, 2026.
mla: Wohlleben, Meike. Hybride Modellierung Der Dynamik Technischer Systeme Auf
Basis von Diskrepanzen. LibreCat University, 2026, doi:10.17619/UNIPB/1-2752.
short: M. Wohlleben, Hybride Modellierung Der Dynamik Technischer Systeme Auf Basis
von Diskrepanzen, LibreCat University, 2026.
date_created: 2026-10-07T11:20:43Z
date_updated: 2026-10-15T10:41:09Z
department:
- _id: '151'
doi: 10.17619/UNIPB/1-2752
language:
- iso: eng
publisher: LibreCat University
status: public
title: Hybride Modellierung der Dynamik technischer Systeme auf Basis von Diskrepanzen
type: dissertation
user_id: '43991'
year: '2026'
...
---
_id: '67301'
author:
- first_name: Meike Claudia
full_name: Wohlleben, Meike Claudia
id: '43991'
last_name: Wohlleben
orcid: 0009-0009-9767-7168
- first_name: Jan
full_name: Schütte, Jan
id: '22109'
last_name: Schütte
orcid: 0000-0001-9025-9742
- first_name: Manuel
full_name: Berkemeier, Manuel
last_name: Berkemeier
- 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: Wohlleben MC, Schütte J, Berkemeier M, Sextro W, Peitz S. Evaluating Physics-Based,
Hybrid, and Data-Driven Models for Rubber-Metal Bushings. Multibody System
Dynamics. Published online 2026. doi:10.1007/s11044-026-10146-9
apa: Wohlleben, M. C., Schütte, J., Berkemeier, M., Sextro, W., & Peitz, S.
(2026). Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal
Bushings. Multibody System Dynamics. https://doi.org/10.1007/s11044-026-10146-9
bibtex: '@article{Wohlleben_Schütte_Berkemeier_Sextro_Peitz_2026, title={Evaluating
Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings}, DOI={10.1007/s11044-026-10146-9},
journal={Multibody System Dynamics}, author={Wohlleben, Meike Claudia and Schütte,
Jan and Berkemeier, Manuel and Sextro, Walter and Peitz, Sebastian}, year={2026}
}'
chicago: Wohlleben, Meike Claudia, Jan Schütte, Manuel Berkemeier, Walter Sextro,
and Sebastian Peitz. “Evaluating Physics-Based, Hybrid, and Data-Driven Models
for Rubber-Metal Bushings.” Multibody System Dynamics, 2026. https://doi.org/10.1007/s11044-026-10146-9.
ieee: 'M. C. Wohlleben, J. Schütte, M. Berkemeier, W. Sextro, and S. Peitz, “Evaluating
Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings,” Multibody
System Dynamics, 2026, doi: 10.1007/s11044-026-10146-9.'
mla: Wohlleben, Meike Claudia, et al. “Evaluating Physics-Based, Hybrid, and Data-Driven
Models for Rubber-Metal Bushings.” Multibody System Dynamics, 2026, doi:10.1007/s11044-026-10146-9.
short: M.C. Wohlleben, J. Schütte, M. Berkemeier, W. Sextro, S. Peitz, Multibody
System Dynamics (2026).
date_created: 2026-10-01T11:53:54Z
date_updated: 2026-10-01T11:54:23Z
department:
- _id: '655'
doi: 10.1007/s11044-026-10146-9
keyword:
- own
- own-journal
language:
- iso: eng
publication: Multibody System Dynamics
status: public
title: Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings
type: journal_article
user_id: '47427'
year: '2026'
...
---
_id: '57829'
abstract:
- lang: eng
text: 'Artificial intelligence (AI) is driving transformative changes across numerous
fields, revolutionizing conventional processes and creating new opportunities
for innovation. The development of mechatronic systems is undergoing a similar
transformation. Over the past decade, modeling, simulation, and optimization techniques
have become integral to the design process, paving the way for the adoption of
AI-based methods. In this paper, we examine the potential for integrating AI into
the engineering design process, using the V-model from the VDI guideline 2206,
considered the state-of-the-art in product design, as a foundation. We identify
and classify AI methods based on their suitability for specific stages within
the engineering product design workflow. Furthermore, we present a series of application
examples where AI-assisted design has been successfully implemented by the authors.
These examples, drawn from research projects within the DFG Priority Program \emph{SPP~2353:
Daring More Intelligence - Design Assistants in Mechanics and Dynamics}, showcase
a diverse range of applications across mechanics and mechatronics, including areas
such as acoustics and robotics.'
author:
- first_name: Kristin M.
full_name: de Payrebrune, Kristin M.
last_name: de Payrebrune
- first_name: Kathrin
full_name: Flaßkamp, Kathrin
last_name: Flaßkamp
- first_name: Tom
full_name: Ströhla, Tom
last_name: Ströhla
- first_name: Thomas
full_name: Sattel, Thomas
last_name: Sattel
- first_name: Dieter
full_name: Bestle, Dieter
last_name: Bestle
- first_name: Benedict
full_name: Röder, Benedict
last_name: Röder
- first_name: Peter
full_name: Eberhard, Peter
last_name: Eberhard
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
- first_name: Marcus
full_name: Stoffel, Marcus
last_name: Stoffel
- first_name: Gulakala
full_name: Rutwik, Gulakala
last_name: Rutwik
- first_name: Borse
full_name: Aditya, Borse
last_name: Aditya
- 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: Maximilian
full_name: Raff, Maximilian
last_name: Raff
- first_name: C. David
full_name: Remy, C. David
last_name: Remy
- first_name: Manish
full_name: Yadav, Manish
last_name: Yadav
- first_name: Merten
full_name: Stender, Merten
last_name: Stender
- first_name: Jan
full_name: van Delden, Jan
last_name: van Delden
- first_name: Timo
full_name: Lüddecke, Timo
last_name: Lüddecke
- first_name: Sabine C.
full_name: Langer, Sabine C.
last_name: Langer
- first_name: Julius
full_name: Schultz, Julius
last_name: Schultz
- first_name: Christopher
full_name: Blech, Christopher
last_name: Blech
citation:
ama: de Payrebrune KM, Flaßkamp K, Ströhla T, et al. The impact of AI on engineering
design procedures for dynamical systems. Technische Mechanik - European Journal
of Engineering Mechanics. 2025;45(1):1-23. doi:10.24352/UB.OVGU-2025-037
apa: de Payrebrune, K. M., Flaßkamp, K., Ströhla, T., Sattel, T., Bestle, D., Röder,
B., Eberhard, P., Peitz, S., Stoffel, M., Rutwik, G., Aditya, B., Wohlleben, M.
C., Sextro, W., Raff, M., Remy, C. D., Yadav, M., Stender, M., van Delden, J.,
Lüddecke, T., … Blech, C. (2025). The impact of AI on engineering design procedures
for dynamical systems. Technische Mechanik - European Journal of Engineering
Mechanics, 45(1), 1–23. https://doi.org/10.24352/UB.OVGU-2025-037
bibtex: '@article{de Payrebrune_Flaßkamp_Ströhla_Sattel_Bestle_Röder_Eberhard_Peitz_Stoffel_Rutwik_et
al._2025, title={The impact of AI on engineering design procedures for dynamical
systems}, volume={45}, DOI={10.24352/UB.OVGU-2025-037},
number={1}, journal={Technische Mechanik - European Journal of Engineering Mechanics},
author={de Payrebrune, Kristin M. and Flaßkamp, Kathrin and Ströhla, Tom and Sattel,
Thomas and Bestle, Dieter and Röder, Benedict and Eberhard, Peter and Peitz, Sebastian
and Stoffel, Marcus and Rutwik, Gulakala and et al.}, year={2025}, pages={1–23}
}'
chicago: 'Payrebrune, Kristin M. de, Kathrin Flaßkamp, Tom Ströhla, Thomas Sattel,
Dieter Bestle, Benedict Röder, Peter Eberhard, et al. “The Impact of AI on Engineering
Design Procedures for Dynamical Systems.” Technische Mechanik - European Journal
of Engineering Mechanics 45, no. 1 (2025): 1–23. https://doi.org/10.24352/UB.OVGU-2025-037.'
ieee: 'K. M. de Payrebrune et al., “The impact of AI on engineering design
procedures for dynamical systems,” Technische Mechanik - European Journal of
Engineering Mechanics, vol. 45, no. 1, pp. 1–23, 2025, doi: 10.24352/UB.OVGU-2025-037.'
mla: de Payrebrune, Kristin M., et al. “The Impact of AI on Engineering Design Procedures
for Dynamical Systems.” Technische Mechanik - European Journal of Engineering
Mechanics, vol. 45, no. 1, 2025, pp. 1–23, doi:10.24352/UB.OVGU-2025-037.
short: K.M. de Payrebrune, K. Flaßkamp, T. Ströhla, T. Sattel, D. Bestle, B. Röder,
P. Eberhard, S. Peitz, M. Stoffel, G. Rutwik, B. Aditya, M.C. Wohlleben, W. Sextro,
M. Raff, C.D. Remy, M. Yadav, M. Stender, J. van Delden, T. Lüddecke, S.C. Langer,
J. Schultz, C. Blech, Technische Mechanik - European Journal of Engineering Mechanics
45 (2025) 1–23.
date_created: 2024-12-18T09:07:41Z
date_updated: 2026-10-07T11:25:00Z
doi: 10.24352/UB.OVGU-2025-037
intvolume: ' 45'
issue: '1'
language:
- iso: eng
page: 1-23
publication: Technische Mechanik - European Journal of Engineering Mechanics
quality_controlled: '1'
status: public
title: The impact of AI on engineering design procedures for dynamical systems
type: journal_article
user_id: '43991'
volume: 45
year: '2025'
...
---
_id: '56113'
abstract:
- lang: eng
text: Abstract This study focuses on hybrid modeling approaches that combine physical
and data-driven methods to create more effective dynamical system models. In particular,
it examines discrepancy models, a type of hybrid model that integrates a physical
system model with data-driven compensation for inaccuracies. The study applies
two discrepancy modeling methods to a multibody system using discrepancies in
the state vector and its time derivative, respectively. As an application example,
a four-bar linkage with nonlinear damping is investigated, using a simplified
conservative system as a physical model. The comparative analysis of the two methods
shows that the continuous approach generally outperforms the discrete method in
terms of accuracy and computational efficiency, especially for velocity prediction
and prediction horizon. However, scenarios, where input signals for training and
testing differ, present nuanced findings. When the continuous method is trained
on complex signals (sine) and tested on simpler ones (stair), it struggles to
deliver satisfactory results, exhibiting notably higher root mean square error
(RMSE) values, particularly in angular velocity prediction. Conversely, training
on simple signals (stair) and testing on complex ones (sine) surprisingly yields
low RMSE values, indicating the continuous method’s adaptability. While the discrete
method aligns more closely with expectations and performs better in certain scenarios,
its results are consistently moderate, neither exceptional nor particularly poor.
The study also introduces a selection framework for choosing the most suitable
algorithm based on the specific characteristics of the modeling task. This framework
provides guidance for researchers and practitioners in leveraging hybrid modeling
effectively. Finally, the study concludes with an outlook on future research directions.
author:
- first_name: Meike Claudia
full_name: Wohlleben, Meike Claudia
id: '43991'
last_name: Wohlleben
orcid: 0009-0009-9767-7168
- first_name: Benedict
full_name: Röder, Benedict
last_name: Röder
- first_name: Henrik
full_name: Ebel, Henrik
last_name: Ebel
- first_name: Lars
full_name: Muth, Lars
id: '77313'
last_name: Muth
orcid: 0000-0002-2938-5616
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
- first_name: Peter
full_name: Eberhard, Peter
last_name: Eberhard
citation:
ama: 'Wohlleben MC, Röder B, Ebel H, Muth L, Sextro W, Eberhard P. Hybrid modeling
of multibody systems: Comparison of two discrepancy models for trajectory prediction.
PAMM. Published online 2024:e202400027. doi:https://doi.org/10.1002/pamm.202400027'
apa: 'Wohlleben, M. C., Röder, B., Ebel, H., Muth, L., Sextro, W., & Eberhard,
P. (2024). Hybrid modeling of multibody systems: Comparison of two discrepancy
models for trajectory prediction. PAMM, e202400027. https://doi.org/10.1002/pamm.202400027'
bibtex: '@article{Wohlleben_Röder_Ebel_Muth_Sextro_Eberhard_2024, title={Hybrid
modeling of multibody systems: Comparison of two discrepancy models for trajectory
prediction}, DOI={https://doi.org/10.1002/pamm.202400027},
journal={PAMM}, author={Wohlleben, Meike Claudia and Röder, Benedict and Ebel,
Henrik and Muth, Lars and Sextro, Walter and Eberhard, Peter}, year={2024}, pages={e202400027}
}'
chicago: 'Wohlleben, Meike Claudia, Benedict Röder, Henrik Ebel, Lars Muth, Walter
Sextro, and Peter Eberhard. “Hybrid Modeling of Multibody Systems: Comparison
of Two Discrepancy Models for Trajectory Prediction.” PAMM, 2024, e202400027.
https://doi.org/10.1002/pamm.202400027.'
ieee: 'M. C. Wohlleben, B. Röder, H. Ebel, L. Muth, W. Sextro, and P. Eberhard,
“Hybrid modeling of multibody systems: Comparison of two discrepancy models for
trajectory prediction,” PAMM, p. e202400027, 2024, doi: https://doi.org/10.1002/pamm.202400027.'
mla: 'Wohlleben, Meike Claudia, et al. “Hybrid Modeling of Multibody Systems: Comparison
of Two Discrepancy Models for Trajectory Prediction.” PAMM, 2024, p. e202400027,
doi:https://doi.org/10.1002/pamm.202400027.'
short: M.C. Wohlleben, B. Röder, H. Ebel, L. Muth, W. Sextro, P. Eberhard, PAMM
(2024) e202400027.
date_created: 2024-09-11T13:38:03Z
date_updated: 2025-02-27T19:54:08Z
doi: https://doi.org/10.1002/pamm.202400027
language:
- iso: eng
page: e202400027
publication: PAMM
quality_controlled: '1'
status: public
title: 'Hybrid modeling of multibody systems: Comparison of two discrepancy models
for trajectory prediction'
type: journal_article
user_id: '77313'
year: '2024'
...
---
_id: '46813'
abstract:
- lang: eng
text: Modelling of dynamic systems plays an important role in many engineering disciplines.
Two different approaches are physical modelling and data‐driven modelling, both
of which have their respective advantages and disadvantages. By combining these
two approaches, hybrid models can be created in which the respective disadvantages
are mitigated, with discrepancy models being a particular subclass. Here, the
basic system behaviour is described physically, that is, in the form of differential
equations. Inaccuracies resulting from insufficient modelling or numerics lead
to a discrepancy between the measurements and the model, which can be compensated
by a data‐driven error correction term. Since discrepancy methods still require
a large amount of measurement data, this paper investigates the extent to which
a single discrepancy model can be trained for a physical model with additional
parameter dependencies without the need for retraining. As an example, a damped
electromagnetic oscillating circuit is used. The physical model is realised by
a differential equation describing the electric current, considering only inductance
and capacitance; dissipation due to resistance is neglected. This creates a discrepancy
between measurement and model, which is corrected by a data‐driven model. In the
experiments, the inductance and the capacity are varied. It is found that the
same data‐driven model can only be used if additional parametric dependencies
in the data‐driven term are considered as well.
author:
- first_name: Meike Claudia
full_name: Wohlleben, Meike Claudia
id: '43991'
last_name: Wohlleben
orcid: 0009-0009-9767-7168
- first_name: Lars
full_name: Muth, Lars
id: '77313'
last_name: Muth
orcid: 0000-0002-2938-5616
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Wohlleben MC, Muth L, Peitz S, Sextro W. Transferability of a discrepancy
model for the dynamics of electromagnetic oscillating circuits. In: Proceedings
in Applied Mathematics and Mechanics. Wiley; 2023. doi:10.1002/pamm.202300039'
apa: Wohlleben, M. C., Muth, L., Peitz, S., & Sextro, W. (2023). Transferability
of a discrepancy model for the dynamics of electromagnetic oscillating circuits.
Proceedings in Applied Mathematics and Mechanics. https://doi.org/10.1002/pamm.202300039
bibtex: '@inproceedings{Wohlleben_Muth_Peitz_Sextro_2023, title={Transferability
of a discrepancy model for the dynamics of electromagnetic oscillating circuits},
DOI={10.1002/pamm.202300039},
booktitle={Proceedings in Applied Mathematics and Mechanics}, publisher={Wiley},
author={Wohlleben, Meike Claudia and Muth, Lars and Peitz, Sebastian and Sextro,
Walter}, year={2023} }'
chicago: Wohlleben, Meike Claudia, Lars Muth, Sebastian Peitz, and Walter Sextro.
“Transferability of a Discrepancy Model for the Dynamics of Electromagnetic Oscillating
Circuits.” In Proceedings in Applied Mathematics and Mechanics. Wiley,
2023. https://doi.org/10.1002/pamm.202300039.
ieee: 'M. C. Wohlleben, L. Muth, S. Peitz, and W. Sextro, “Transferability of a
discrepancy model for the dynamics of electromagnetic oscillating circuits,” 2023,
doi: 10.1002/pamm.202300039.'
mla: Wohlleben, Meike Claudia, et al. “Transferability of a Discrepancy Model for
the Dynamics of Electromagnetic Oscillating Circuits.” Proceedings in Applied
Mathematics and Mechanics, Wiley, 2023, doi:10.1002/pamm.202300039.
short: 'M.C. Wohlleben, L. Muth, S. Peitz, W. Sextro, in: Proceedings in Applied
Mathematics and Mechanics, Wiley, 2023.'
date_created: 2023-09-06T05:18:05Z
date_updated: 2023-09-21T14:47:20Z
department:
- _id: '655'
- _id: '151'
doi: 10.1002/pamm.202300039
keyword:
- Electrical and Electronic Engineering
- Atomic and Molecular Physics
- and Optics
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://onlinelibrary.wiley.com/doi/epdf/10.1002/pamm.202300039
oa: '1'
publication: Proceedings in Applied Mathematics and Mechanics
publication_identifier:
issn:
- 1617-7061
- 1617-7061
publication_status: published
publisher: Wiley
quality_controlled: '1'
status: public
title: Transferability of a discrepancy model for the dynamics of electromagnetic
oscillating circuits
type: conference
user_id: '77313'
year: '2023'
...
---
_id: '29727'
author:
- first_name: Meike Claudia
full_name: Wohlleben, Meike Claudia
id: '43991'
last_name: Wohlleben
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Wohlleben MC, Bender A, Peitz S, Sextro W. Development of a Hybrid Modeling
Methodology for Oscillating Systems with Friction. In: Machine Learning, Optimization,
and Data Science. Springer International Publishing; 2022. doi:10.1007/978-3-030-95470-3_8'
apa: Wohlleben, M. C., Bender, A., Peitz, S., & Sextro, W. (2022). Development
of a Hybrid Modeling Methodology for Oscillating Systems with Friction. In Machine
Learning, Optimization, and Data Science. Springer International Publishing.
https://doi.org/10.1007/978-3-030-95470-3_8
bibtex: '@inbook{Wohlleben_Bender_Peitz_Sextro_2022, place={Cham}, title={Development
of a Hybrid Modeling Methodology for Oscillating Systems with Friction}, DOI={10.1007/978-3-030-95470-3_8},
booktitle={Machine Learning, Optimization, and Data Science}, publisher={Springer
International Publishing}, author={Wohlleben, Meike Claudia and Bender, Amelie
and Peitz, Sebastian and Sextro, Walter}, year={2022} }'
chicago: 'Wohlleben, Meike Claudia, Amelie Bender, Sebastian Peitz, and Walter Sextro.
“Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction.”
In Machine Learning, Optimization, and Data Science. Cham: Springer International
Publishing, 2022. https://doi.org/10.1007/978-3-030-95470-3_8.'
ieee: 'M. C. Wohlleben, A. Bender, S. Peitz, and W. Sextro, “Development of a Hybrid
Modeling Methodology for Oscillating Systems with Friction,” in Machine Learning,
Optimization, and Data Science, Cham: Springer International Publishing, 2022.'
mla: Wohlleben, Meike Claudia, et al. “Development of a Hybrid Modeling Methodology
for Oscillating Systems with Friction.” Machine Learning, Optimization, and
Data Science, Springer International Publishing, 2022, doi:10.1007/978-3-030-95470-3_8.
short: 'M.C. Wohlleben, A. Bender, S. Peitz, W. Sextro, in: Machine Learning, Optimization,
and Data Science, Springer International Publishing, Cham, 2022.'
date_created: 2022-02-03T10:30:23Z
date_updated: 2023-04-26T12:10:58Z
department:
- _id: '151'
- _id: '655'
doi: 10.1007/978-3-030-95470-3_8
language:
- iso: eng
main_file_link:
- url: https://link.springer.com/content/pdf/10.1007%2F978-3-030-95470-3_8.pdf
place: Cham
publication: Machine Learning, Optimization, and Data Science
publication_identifier:
isbn:
- '9783030954697'
- '9783030954703'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer International Publishing
quality_controlled: '1'
status: public
title: Development of a Hybrid Modeling Methodology for Oscillating Systems with Friction
type: book_chapter
user_id: '43991'
year: '2022'
...
---
_id: '27111'
abstract:
- lang: eng
text: In the industry 4.0 era, there is a growing need to transform unstructured
data acquired by a multitude of sources into information and subsequently into
knowledge to improve the quality of manufactured products, to boost production,
for predictive maintenance, etc. Data-driven approaches, such as machine learning
techniques, are typically employed to model the underlying relationship from data.
However, an increase in model accuracy with state-of-the-art methods, such as
deep convolutional neural networks, results in less interpretability and transparency.
Due to the ease of implementation, interpretation and transparency to both domain
experts and non-experts, a rule-based method is proposed in this paper, for prognostics
and health management (PHM) and specifically for diagnostics. The proposed method
utilizes the most relevant sensor signals acquired via feature extraction and
selection techniques and expert knowledge. As a case study, the presented method
is evaluated on data from a real-world quality control set-up provided by the
European prognostics and health management society (PHME) at the conference’s
2021 data challenge. With the proposed method, our team took the third place,
capable of successfully diagnosing different fault modes, irrespective of varying
conditions.
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Lars
full_name: Muth, Lars
id: '77313'
last_name: Muth
orcid: 0000-0002-2938-5616
- first_name: Meike Claudia
full_name: Wohlleben, Meike Claudia
id: '43991'
last_name: Wohlleben
orcid: 0009-0009-9767-7168
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Aimiyekagbon OK, Muth L, Wohlleben MC, Bender A, Sextro W. Rule-based Diagnostics
of a Production Line. In: Do P, King S, Fink O, eds. Proceedings of the European
Conference of the PHM Society 2021. Vol 6. ; 2021:527-536. doi:10.36001/phme.2021.v6i1.3042'
apa: Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., & Sextro,
W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, &
O. Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021
(Vol. 6, Issue 1, pp. 527–536). https://doi.org/10.36001/phme.2021.v6i1.3042
bibtex: '@inproceedings{Aimiyekagbon_Muth_Wohlleben_Bender_Sextro_2021, title={Rule-based
Diagnostics of a Production Line}, volume={6}, DOI={10.36001/phme.2021.v6i1.3042},
number={1}, booktitle={Proceedings of the European Conference of the PHM Society
2021}, author={Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike
Claudia and Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve
and Fink, Olga}, year={2021}, pages={527–536} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Lars Muth, Meike Claudia Wohlleben, Amelie
Bender, and Walter Sextro. “Rule-Based Diagnostics of a Production Line.” In Proceedings
of the European Conference of the PHM Society 2021, edited by Phuc Do, Steve
King, and Olga Fink, 6:527–36, 2021. https://doi.org/10.36001/phme.2021.v6i1.3042.
ieee: 'O. K. Aimiyekagbon, L. Muth, M. C. Wohlleben, A. Bender, and W. Sextro, “Rule-based
Diagnostics of a Production Line,” in Proceedings of the European Conference
of the PHM Society 2021, 2021, vol. 6, no. 1, pp. 527–536, doi: 10.36001/phme.2021.v6i1.3042.'
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Rule-Based Diagnostics of a Production
Line.” Proceedings of the European Conference of the PHM Society 2021,
edited by Phuc Do et al., vol. 6, no. 1, 2021, pp. 527–36, doi:10.36001/phme.2021.v6i1.3042.
short: 'O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: P.
Do, S. King, O. Fink (Eds.), Proceedings of the European Conference of the PHM
Society 2021, 2021, pp. 527–536.'
conference:
name: PHM Society European Conference
date_created: 2021-11-03T12:26:39Z
date_updated: 2023-09-22T09:13:01Z
department:
- _id: '151'
doi: 10.36001/phme.2021.v6i1.3042
editor:
- first_name: Phuc
full_name: Do, Phuc
last_name: Do
- first_name: Steve
full_name: King, Steve
last_name: King
- first_name: Olga
full_name: Fink, Olga
last_name: Fink
intvolume: ' 6'
issue: '1'
keyword:
- PHME 2021
- Feature Selection Classification
- Feature Selection Clustering
- Interpretable Model
- Transparent Model
- Industry 4.0
- Real-World Diagnostics
- Quality Control
- Predictive Maintenance
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://papers.phmsociety.org/index.php/phme/article/download/3042/1812
oa: '1'
page: 527-536
publication: Proceedings of the European Conference of the PHM Society 2021
publication_status: published
quality_controlled: '1'
status: public
title: Rule-based Diagnostics of a Production Line
type: conference
user_id: '9557'
volume: 6
year: '2021'
...