---
_id: '62885'
author:
- first_name: Malin
full_name: Osnabrügge, Malin
id: '79748'
last_name: Osnabrügge
- first_name: Claudia
full_name: Tenberge, Claudia
id: '67302'
last_name: Tenberge
- first_name: Sabine
full_name: Fechner, Sabine
id: '54823'
last_name: Fechner
orcid: 0000-0001-5645-5870
citation:
ama: 'Osnabrügge M, Tenberge C, Fechner S. Artificial Intelligence in primary science
and technology education with a focus on implementation of AI in a learning context
– Results of a scoping review. In: ; 2026.'
apa: Osnabrügge, M., Tenberge, C., & Fechner, S. (2026). Artificial Intelligence
in primary science and technology education with a focus on implementation of
AI in a learning context – Results of a scoping review. Pupils’ Attitudes
Towards Technology (PATT), Norrköping, Sweden.
bibtex: '@inproceedings{Osnabrügge_Tenberge_Fechner_2026, title={Artificial Intelligence
in primary science and technology education with a focus on implementation of
AI in a learning context – Results of a scoping review}, author={Osnabrügge, Malin
and Tenberge, Claudia and Fechner, Sabine}, year={2026} }'
chicago: Osnabrügge, Malin, Claudia Tenberge, and Sabine Fechner. “Artificial Intelligence
in Primary Science and Technology Education with a Focus on Implementation of
AI in a Learning Context – Results of a Scoping Review,” 2026.
ieee: M. Osnabrügge, C. Tenberge, and S. Fechner, “Artificial Intelligence in primary
science and technology education with a focus on implementation of AI in a learning
context – Results of a scoping review,” presented at the Pupils’ Attitudes Towards
Technology (PATT), Norrköping, Sweden, 2026.
mla: Osnabrügge, Malin, et al. Artificial Intelligence in Primary Science and
Technology Education with a Focus on Implementation of AI in a Learning Context
– Results of a Scoping Review. 2026.
short: 'M. Osnabrügge, C. Tenberge, S. Fechner, in: 2026.'
conference:
end_date: 2026-06-18
location: Norrköping, Sweden
name: Pupils' Attitudes Towards Technology (PATT)
start_date: 2026-06-15
date_created: 2025-12-04T14:12:38Z
date_updated: 2026-09-29T16:03:29Z
department:
- _id: '386'
- _id: '588'
- _id: '33'
keyword:
- Artificial intelligence
- primary education
- science and technology education
language:
- iso: eng
publication_status: published
quality_controlled: '1'
status: public
title: Artificial Intelligence in primary science and technology education with a
focus on implementation of AI in a learning context – Results of a scoping review
type: conference
user_id: '54823'
year: '2026'
...
---
_id: '67091'
citation:
ama: Schulze M, ed. Ida Büngener 1963 – 2024 Selected Works . 800th ed.
Distanz; 2026.
apa: Schulze, M. (Ed.). (2026). Ida Büngener 1963 – 2024 Selected Works
(800th ed.). Distanz.
bibtex: '@book{Schulze_2026, place={Berlin}, edition={800}, title={ Ida Büngener
1963 – 2024 Selected Works }, publisher={Distanz}, year={2026} }'
chicago: 'Schulze, Max, ed. Ida Büngener 1963 – 2024 Selected Works . 800th
ed. Berlin: Distanz, 2026.'
ieee: 'M. Schulze, Ed., Ida Büngener 1963 – 2024 Selected Works , 800th
ed. Berlin: Distanz, 2026.'
mla: Schulze, Max, editor. Ida Büngener 1963 – 2024 Selected Works . 800th
ed., Distanz, 2026.
short: M. Schulze, ed., Ida Büngener 1963 – 2024 Selected Works , 800th ed., Distanz,
Berlin, 2026.
date_created: 2026-09-09T15:04:11Z
date_updated: 2026-09-29T20:13:35Z
edition: '800'
editor:
- first_name: Max
full_name: Schulze, Max
id: '77991'
last_name: Schulze
keyword:
- Malerei
- Zeichnung
language:
- iso: eng
- iso: ger
page: '308'
place: Berlin
publication_identifier:
isbn:
- 978-3-95476-872-1
publication_status: published
publisher: Distanz
status: public
title: ' Ida Büngener 1963 – 2024 Selected Works '
type: book_editor
user_id: '77991'
year: '2026'
...
---
_id: '62948'
author:
- first_name: Pascal
full_name: Pollmeier, Pascal
id: '44191'
last_name: Pollmeier
- first_name: Jonas
full_name: Ponath, Jonas
id: '100087'
last_name: Ponath
- first_name: Claudia
full_name: Bohrmann-Linde, Claudia
last_name: Bohrmann-Linde
- first_name: Isabel
full_name: Rubner, Isabel
last_name: Rubner
- first_name: Katrin
full_name: Sommer, Katrin
last_name: Sommer
- first_name: Sabine
full_name: Fechner, Sabine
id: '54823'
last_name: Fechner
orcid: 0000-0001-5645-5870
citation:
ama: 'Pollmeier P, Ponath J, Bohrmann-Linde C, Rubner I, Sommer K, Fechner S. Digital
und praxisnah: Was Chemielehrkräfte sich von (digitalisierungsbezogenen) Fortbildungen
wünschen. CHEMKON. 2026;33(5):138-144. doi:https://doi.org/10.1002/ckon.70019'
apa: 'Pollmeier, P., Ponath, J., Bohrmann-Linde, C., Rubner, I., Sommer, K., &
Fechner, S. (2026). Digital und praxisnah: Was Chemielehrkräfte sich von (digitalisierungsbezogenen)
Fortbildungen wünschen. CHEMKON, 33(5), 138–144. https://doi.org/10.1002/ckon.70019'
bibtex: '@article{Pollmeier_Ponath_Bohrmann-Linde_Rubner_Sommer_Fechner_2026, title={Digital
und praxisnah: Was Chemielehrkräfte sich von (digitalisierungsbezogenen) Fortbildungen
wünschen}, volume={33}, DOI={https://doi.org/10.1002/ckon.70019},
number={5}, journal={CHEMKON}, author={Pollmeier, Pascal and Ponath, Jonas and
Bohrmann-Linde, Claudia and Rubner, Isabel and Sommer, Katrin and Fechner, Sabine},
year={2026}, pages={138–144} }'
chicago: 'Pollmeier, Pascal, Jonas Ponath, Claudia Bohrmann-Linde, Isabel Rubner,
Katrin Sommer, and Sabine Fechner. “Digital und praxisnah: Was Chemielehrkräfte
sich von (digitalisierungsbezogenen) Fortbildungen wünschen.” CHEMKON 33,
no. 5 (2026): 138–44. https://doi.org/10.1002/ckon.70019.'
ieee: 'P. Pollmeier, J. Ponath, C. Bohrmann-Linde, I. Rubner, K. Sommer, and S.
Fechner, “Digital und praxisnah: Was Chemielehrkräfte sich von (digitalisierungsbezogenen)
Fortbildungen wünschen,” CHEMKON, vol. 33, no. 5, pp. 138–144, 2026, doi:
https://doi.org/10.1002/ckon.70019.'
mla: 'Pollmeier, Pascal, et al. “Digital und praxisnah: Was Chemielehrkräfte sich
von (digitalisierungsbezogenen) Fortbildungen wünschen.” CHEMKON, vol.
33, no. 5, 2026, pp. 138–44, doi:https://doi.org/10.1002/ckon.70019.'
short: P. Pollmeier, J. Ponath, C. Bohrmann-Linde, I. Rubner, K. Sommer, S. Fechner,
CHEMKON 33 (2026) 138–144.
date_created: 2025-12-08T08:57:11Z
date_updated: 2026-09-30T17:12:59Z
department:
- _id: '386'
doi: https://doi.org/10.1002/ckon.70019
intvolume: ' 33'
issue: '5'
keyword:
- Digital
- Digitalisierung
- Künstliche Intelligenz
- KI
- Messsensoren
- Fortbildung
- Lehrkräfte
- Chemie
language:
- iso: ger
main_file_link:
- open_access: '1'
oa: '1'
page: 138-144
project:
- _id: '641'
name: ComeMINT-Netzwerk. fortbilden durch vernetzen – vernetzen durch fortbilden.
Gelingensbedingungen adaptiver MINT-Fortbildungsmodule in Community Networks.
publication: CHEMKON
publication_status: published
quality_controlled: '1'
status: public
title: 'Digital und praxisnah: Was Chemielehrkräfte sich von (digitalisierungsbezogenen)
Fortbildungen wünschen'
type: journal_article
user_id: '54823'
volume: 33
year: '2026'
...
---
_id: '67066'
article_number: '112568'
author:
- first_name: Gunter
full_name: Kullmer, Gunter
id: '291'
last_name: Kullmer
- first_name: Sven
full_name: Krome, Sven
id: '57245'
last_name: Krome
- first_name: Richard
full_name: Ostwald, Richard
id: '106876'
last_name: Ostwald
orcid: 0000-0003-2147-8444
citation:
ama: Kullmer G, Krome S, Ostwald R. A new approach for the formulaic description
of the crack growth rate curve for long cracks in aluminum alloys. Engineering
Fracture Mechanics. 2026;345. doi:10.1016/j.engfracmech.2026.112568
apa: Kullmer, G., Krome, S., & Ostwald, R. (2026). A new approach for the formulaic
description of the crack growth rate curve for long cracks in aluminum alloys.
Engineering Fracture Mechanics, 345, Article 112568. https://doi.org/10.1016/j.engfracmech.2026.112568
bibtex: '@article{Kullmer_Krome_Ostwald_2026, title={A new approach for the formulaic
description of the crack growth rate curve for long cracks in aluminum alloys},
volume={345}, DOI={10.1016/j.engfracmech.2026.112568},
number={112568}, journal={Engineering Fracture Mechanics}, publisher={Elsevier
BV}, author={Kullmer, Gunter and Krome, Sven and Ostwald, Richard}, year={2026}
}'
chicago: Kullmer, Gunter, Sven Krome, and Richard Ostwald. “A New Approach for the
Formulaic Description of the Crack Growth Rate Curve for Long Cracks in Aluminum
Alloys.” Engineering Fracture Mechanics 345 (2026). https://doi.org/10.1016/j.engfracmech.2026.112568.
ieee: 'G. Kullmer, S. Krome, and R. Ostwald, “A new approach for the formulaic description
of the crack growth rate curve for long cracks in aluminum alloys,” Engineering
Fracture Mechanics, vol. 345, Art. no. 112568, 2026, doi: 10.1016/j.engfracmech.2026.112568.'
mla: Kullmer, Gunter, et al. “A New Approach for the Formulaic Description of the
Crack Growth Rate Curve for Long Cracks in Aluminum Alloys.” Engineering Fracture
Mechanics, vol. 345, 112568, Elsevier BV, 2026, doi:10.1016/j.engfracmech.2026.112568.
short: G. Kullmer, S. Krome, R. Ostwald, Engineering Fracture Mechanics 345 (2026).
date_created: 2026-09-08T04:52:28Z
date_updated: 2026-09-30T13:37:39Z
department:
- _id: '9'
- _id: '952'
- _id: '321'
doi: 10.1016/j.engfracmech.2026.112568
intvolume: ' 345'
language:
- iso: eng
project:
- _id: '132'
name: TRR 285 - Project Area B
- _id: '143'
name: TRR 285 - Subproject B04
- _id: '130'
name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
Prozessketten'
publication: Engineering Fracture Mechanics
publication_identifier:
issn:
- 0013-7944
publication_status: published
publisher: Elsevier BV
quality_controlled: '1'
status: public
title: A new approach for the formulaic description of the crack growth rate curve
for long cracks in aluminum alloys
type: journal_article
user_id: '57245'
volume: 345
year: '2026'
...
---
_id: '62821'
citation:
ama: Vogelsang C, Grotegut L, Bruns J, Riese J, Fechner S, eds. Handlungsorientierung
in Der Ausbildung von Lehrkräften Und Pädagogischen Fachkräften. Vol 2. Waxmann;
2026. doi:https://doi.org/10.31244/9783818851057
apa: Vogelsang, C., Grotegut, L., Bruns, J., Riese, J., & Fechner, S. (Eds.).
(2026). Handlungsorientierung in der Ausbildung von Lehrkräften und pädagogischen
Fachkräften (Vol. 2). Waxmann. https://doi.org/10.31244/9783818851057
bibtex: '@book{Vogelsang_Grotegut_Bruns_Riese_Fechner_2026, place={Münster}, series={Paderborner
Beiträge zur Bildungsforschung und Lehrkräftebildung}, title={Handlungsorientierung
in der Ausbildung von Lehrkräften und pädagogischen Fachkräften}, volume={2},
DOI={https://doi.org/10.31244/9783818851057},
publisher={Waxmann}, year={2026}, collection={Paderborner Beiträge zur Bildungsforschung
und Lehrkräftebildung} }'
chicago: 'Vogelsang, Christoph, Lea Grotegut, Julia Bruns, Josef Riese, and Sabine
Fechner, eds. Handlungsorientierung in Der Ausbildung von Lehrkräften Und Pädagogischen
Fachkräften. Vol. 2. Paderborner Beiträge Zur Bildungsforschung Und Lehrkräftebildung.
Münster: Waxmann, 2026. https://doi.org/10.31244/9783818851057.'
ieee: 'C. Vogelsang, L. Grotegut, J. Bruns, J. Riese, and S. Fechner, Eds., Handlungsorientierung
in der Ausbildung von Lehrkräften und pädagogischen Fachkräften, vol. 2. Münster:
Waxmann, 2026.'
mla: Vogelsang, Christoph, et al., editors. Handlungsorientierung in Der Ausbildung
von Lehrkräften Und Pädagogischen Fachkräften. Waxmann, 2026, doi:https://doi.org/10.31244/9783818851057.
short: C. Vogelsang, L. Grotegut, J. Bruns, J. Riese, S. Fechner, eds., Handlungsorientierung
in Der Ausbildung von Lehrkräften Und Pädagogischen Fachkräften, Waxmann, Münster,
2026.
date_created: 2025-12-03T21:25:03Z
date_updated: 2026-09-30T17:13:17Z
department:
- _id: '33'
doi: https://doi.org/10.31244/9783818851057
editor:
- first_name: Christoph
full_name: Vogelsang, Christoph
id: '4245'
last_name: Vogelsang
orcid: 0000-0002-5804-1855
- first_name: Lea
full_name: Grotegut, Lea
id: '34280'
last_name: Grotegut
- first_name: Julia
full_name: Bruns, Julia
id: '72183'
last_name: Bruns
orcid: https://orcid.org/0000-0002-6604-5864
- first_name: Josef
full_name: Riese, Josef
id: '429'
last_name: Riese
orcid: 0000-0003-2927-2619
- first_name: Sabine
full_name: Fechner, Sabine
id: '54823'
last_name: Fechner
orcid: 0000-0001-5645-5870
intvolume: ' 2'
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
place: Münster
publication_status: published
publisher: Waxmann
quality_controlled: '1'
series_title: Paderborner Beiträge zur Bildungsforschung und Lehrkräftebildung
status: public
title: Handlungsorientierung in der Ausbildung von Lehrkräften und pädagogischen Fachkräften
type: book_editor
user_id: '54823'
volume: 2
year: '2026'
...
---
_id: '62957'
alternative_title:
- Analyse der Modellierungsprozesse von Grundschüler:innen zum Thema Löslichkeit
author:
- first_name: Julia
full_name: Elsner, Julia
id: '54277'
last_name: Elsner
- first_name: Claudia
full_name: Tenberge, Claudia
id: '67302'
last_name: Tenberge
- first_name: Sabine
full_name: Fechner, Sabine
id: '54823'
last_name: Fechner
orcid: 0000-0001-5645-5870
citation:
ama: Elsner J, Tenberge C, Fechner S. Modellieren und Denken im Diskontinuum. Zeitschrift
für Didaktik der Naturwissenschaften. 2026;32(1):1-17. doi:10.1007/s40573-026-00194-1
apa: Elsner, J., Tenberge, C., & Fechner, S. (2026). Modellieren und Denken
im Diskontinuum. Zeitschrift für Didaktik der Naturwissenschaften, 32(1),
1–17. https://doi.org/10.1007/s40573-026-00194-1
bibtex: '@article{Elsner_Tenberge_Fechner_2026, title={Modellieren und Denken im
Diskontinuum}, volume={32}, DOI={10.1007/s40573-026-00194-1},
number={1}, journal={Zeitschrift für Didaktik der Naturwissenschaften}, author={Elsner,
Julia and Tenberge, Claudia and Fechner, Sabine}, year={2026}, pages={1–17} }'
chicago: 'Elsner, Julia, Claudia Tenberge, and Sabine Fechner. “Modellieren und
Denken im Diskontinuum.” Zeitschrift für Didaktik der Naturwissenschaften
32, no. 1 (2026): 1–17. https://doi.org/10.1007/s40573-026-00194-1.'
ieee: 'J. Elsner, C. Tenberge, and S. Fechner, “Modellieren und Denken im Diskontinuum,”
Zeitschrift für Didaktik der Naturwissenschaften, vol. 32, no. 1, pp. 1–17,
2026, doi: 10.1007/s40573-026-00194-1.'
mla: Elsner, Julia, et al. “Modellieren und Denken im Diskontinuum.” Zeitschrift
für Didaktik der Naturwissenschaften, vol. 32, no. 1, 2026, pp. 1–17, doi:10.1007/s40573-026-00194-1.
short: J. Elsner, C. Tenberge, S. Fechner, Zeitschrift für Didaktik der Naturwissenschaften
32 (2026) 1–17.
date_created: 2025-12-08T09:33:10Z
date_updated: 2026-09-30T17:14:52Z
department:
- _id: '386'
- _id: '588'
doi: 10.1007/s40573-026-00194-1
intvolume: ' 32'
issue: '1'
language:
- iso: ger
main_file_link:
- open_access: '1'
oa: '1'
page: 1-17
publication: Zeitschrift für Didaktik der Naturwissenschaften
publication_status: published
quality_controlled: '1'
status: public
title: Modellieren und Denken im Diskontinuum
type: journal_article
user_id: '54823'
volume: 32
year: '2026'
...
---
_id: '62956'
article_type: original
author:
- first_name: Pascal
full_name: Pollmeier, Pascal
id: '44191'
last_name: Pollmeier
- first_name: Talea
full_name: Schulte, Talea
last_name: Schulte
- first_name: Jonas
full_name: Ponath, Jonas
id: '100087'
last_name: Ponath
- first_name: Sabine
full_name: Fechner, Sabine
id: '54823'
last_name: Fechner
orcid: 0000-0001-5645-5870
citation:
ama: Pollmeier P, Schulte T, Ponath J, Fechner S. Lernprozesse im Chemieunterricht
durch kontextorientierte digitale Lernumgebungen mit Messwerterfassung unterstützen.
Naturwissenschaften im Unterricht - Chemie. Published online 2026.
apa: Pollmeier, P., Schulte, T., Ponath, J., & Fechner, S. (2026). Lernprozesse
im Chemieunterricht durch kontextorientierte digitale Lernumgebungen mit Messwerterfassung
unterstützen. Naturwissenschaften Im Unterricht - Chemie.
bibtex: '@article{Pollmeier_Schulte_Ponath_Fechner_2026, title={Lernprozesse im
Chemieunterricht durch kontextorientierte digitale Lernumgebungen mit Messwerterfassung
unterstützen}, journal={Naturwissenschaften im Unterricht - Chemie}, author={Pollmeier,
Pascal and Schulte, Talea and Ponath, Jonas and Fechner, Sabine}, year={2026}
}'
chicago: Pollmeier, Pascal, Talea Schulte, Jonas Ponath, and Sabine Fechner. “Lernprozesse
Im Chemieunterricht Durch Kontextorientierte Digitale Lernumgebungen Mit Messwerterfassung
Unterstützen.” Naturwissenschaften Im Unterricht - Chemie, 2026.
ieee: P. Pollmeier, T. Schulte, J. Ponath, and S. Fechner, “Lernprozesse im Chemieunterricht
durch kontextorientierte digitale Lernumgebungen mit Messwerterfassung unterstützen,”
Naturwissenschaften im Unterricht - Chemie, 2026.
mla: Pollmeier, Pascal, et al. “Lernprozesse Im Chemieunterricht Durch Kontextorientierte
Digitale Lernumgebungen Mit Messwerterfassung Unterstützen.” Naturwissenschaften
Im Unterricht - Chemie, 2026.
short: P. Pollmeier, T. Schulte, J. Ponath, S. Fechner, Naturwissenschaften Im Unterricht
- Chemie (2026).
date_created: 2025-12-08T09:30:27Z
date_updated: 2026-09-30T17:15:46Z
department:
- _id: '386'
keyword:
- Digital
- Digitalisierung
- Nachhaltigkeit
- Bildung für nachhaltige Entwicklung
- BNE
- Lernumgebungen
language:
- iso: eng
project:
- _id: '641'
name: ComeMINT-Netzwerk. fortbilden durch vernetzen – vernetzen durch fortbilden.
Gelingensbedingungen adaptiver MINT-Fortbildungsmodule in Community Networks.
publication: Naturwissenschaften im Unterricht - Chemie
publication_status: published
quality_controlled: '1'
status: public
title: Lernprozesse im Chemieunterricht durch kontextorientierte digitale Lernumgebungen
mit Messwerterfassung unterstützen
type: journal_article
user_id: '54823'
year: '2026'
...
---
_id: '67295'
abstract:
- lang: eng
text: In increasingly volatile and uncertain markets, corporate resilience has become
a critical capability in strategic product planning. Companies face significant
challenges in systematically monitoring and interpreting heterogeneous environmental
data originating from diverse sources, formats, and temporal contexts. While predefined
workflows and decision trees can support strategic analysis, they often lack the
flexibility required to cope with dynamic market conditions and foresightrelated
data from extreme dispersed and heterogeneous sources. This paper proposes a method
to enhance corporate resilience through the application of generic, reusable AI-based
workflows in strategic product planning. The approach integrates Data Science
and Artificial Intelligence methods into modular, visually modelled workflows
that enable hybrid human-AI decision-making. Based on a systematic literature
review and an analysis of industrial challenges, key success factors and resilience
criteria are identified. These insights are used to develop a method that supports
internal and external analyses, scenario-based strategy development, and adaptive
implementation monitoring within a generic workflow structure. The method leverages
techniques such as machine learning and generative AI to process structured and
unstructured data, identify patterns, and support real-time strategic assessments.
Validation with decision-makers from medium-sized companies demonstrates improved
transparency, repeatability, and cross-functional collaboration compared to predefined
workflows.
author:
- first_name: Iris
full_name: Gräßler, Iris
id: '47565'
last_name: Gräßler
orcid: 0000-0001-5765-971X
- first_name: Deniz
full_name: Özcan, Deniz
id: '58595'
last_name: Özcan
citation:
ama: 'Gräßler I, Özcan D. Corporate resilience through generic AI-based workflows
in strategic product planning. In: Gräßler I, ed. 1st International Symposium:
March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University. Vol 1.
LibreCat University; 2026. doi:10.17619/UNIPB/1-2636'
apa: 'Gräßler, I., & Özcan, D. (2026). Corporate resilience through generic
AI-based workflows in strategic product planning. In I. Gräßler (Ed.), 1st
International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn
University (Vol. 1). LibreCat University. https://doi.org/10.17619/UNIPB/1-2636'
bibtex: '@inproceedings{Gräßler_Özcan_2026, title={Corporate resilience through
generic AI-based workflows in strategic product planning}, volume={1}, DOI={10.17619/UNIPB/1-2636}, booktitle={1st
International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn
University}, publisher={LibreCat University}, author={Gräßler, Iris and Özcan,
Deniz}, editor={Gräßler, Iris}, year={2026} }'
chicago: 'Gräßler, Iris, and Deniz Özcan. “Corporate Resilience through Generic
AI-Based Workflows in Strategic Product Planning.” In 1st International Symposium:
March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University, edited
by Iris Gräßler, Vol. 1. LibreCat University, 2026. https://doi.org/10.17619/UNIPB/1-2636.'
ieee: 'I. Gräßler and D. Özcan, “Corporate resilience through generic AI-based workflows
in strategic product planning,” in 1st International Symposium: March 24 –
26, 2026, Heinz Nixdorf Institute, Paderborn University, Paderborn, 2026,
vol. 1, doi: 10.17619/UNIPB/1-2636.'
mla: 'Gräßler, Iris, and Deniz Özcan. “Corporate Resilience through Generic AI-Based
Workflows in Strategic Product Planning.” 1st International Symposium: March
24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University, edited by Iris
Gräßler, vol. 1, LibreCat University, 2026, doi:10.17619/UNIPB/1-2636.'
short: 'I. Gräßler, D. Özcan, in: I. Gräßler (Ed.), 1st International Symposium:
March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University, LibreCat University,
2026.'
conference:
end_date: 2026-03-26
location: Paderborn
name: International Symposium on Hybrid Intelligence in Product and Production Engineering
1. 2026 Paderborn
start_date: 2026-03-24
date_created: 2026-10-01T10:04:57Z
date_updated: 2026-10-01T10:14:12Z
department:
- _id: '43'
- _id: '9'
- _id: '26'
- _id: '152'
doi: 10.17619/UNIPB/1-2636
editor:
- first_name: Iris
full_name: Gräßler, Iris
last_name: Gräßler
intvolume: ' 1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://digital.ub.uni-paderborn.de/hs/download/pdf/8359163
oa: '1'
publication: '1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute,
Paderborn University'
publisher: LibreCat University
quality_controlled: '1'
status: public
title: Corporate resilience through generic AI-based workflows in strategic product
planning
type: conference
user_id: '58595'
volume: 1
year: '2026'
...
---
_id: '67300'
abstract:
- lang: eng
text: Deep neural networks (DNNs) are vulnerable to small adversarial perturbations,
which are tiny changes to the input data that appear insignificant but cause the
model to produce drastically different outputs. Many defense methods require modifying
model architectures during evaluation or performing test-time data purification.
This not only introduces additional complexity but is often architecture-dependent.
We show, however, that robust feature learning during training can significantly
enhance DNN robustness. We propose MOREL, a multi-objective approach that aligns
natural and adversarial features using cosine similarity and multi-positive contrastive
losses to encourage similar features for same-class inputs. Extensive experiments
demonstrate that MOREL significantly improves robustness against both white-box
and black-box attacks. Our code is available at https://github.com/salomonhotegni/MOREL.
author:
- first_name: Sedjro Salomon
full_name: Hotegni, Sedjro Salomon
id: '97995'
last_name: Hotegni
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
citation:
ama: 'Hotegni SS, Peitz S. Enhancing Adversarial Robustness Through Multi-objective
Representation Learning. In: Senn W, Sanguineti M, Saudargiene A, et al., eds.
Artificial Neural Networks and Machine Learning – ICANN 2025. Springer
Nature Switzerland; 2026:442–454. doi:10.1007/978-3-032-04558-4_35'
apa: Hotegni, S. S., & Peitz, S. (2026). Enhancing Adversarial Robustness Through
Multi-objective Representation Learning. 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. 442–454). Springer Nature
Switzerland. https://doi.org/10.1007/978-3-032-04558-4_35
bibtex: '@inproceedings{Hotegni_Peitz_2026, place={Cham}, title={Enhancing Adversarial
Robustness Through Multi-objective Representation Learning}, DOI={10.1007/978-3-032-04558-4_35},
booktitle={Artificial Neural Networks and Machine Learning – ICANN 2025}, publisher={Springer
Nature Switzerland}, author={Hotegni, Sedjro Salomon 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={442–454}
}'
chicago: 'Hotegni, Sedjro Salomon, and Sebastian Peitz. “Enhancing Adversarial Robustness
Through Multi-Objective Representation Learning.” 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, 442–454. Cham: Springer Nature Switzerland, 2026. https://doi.org/10.1007/978-3-032-04558-4_35.'
ieee: 'S. S. Hotegni and S. Peitz, “Enhancing Adversarial Robustness Through Multi-objective
Representation Learning,” in Artificial Neural Networks and Machine Learning
– ICANN 2025, 2026, pp. 442–454, doi: 10.1007/978-3-032-04558-4_35.'
mla: Hotegni, Sedjro Salomon, and Sebastian Peitz. “Enhancing Adversarial Robustness
Through Multi-Objective Representation Learning.” Artificial Neural Networks
and Machine Learning – ICANN 2025, edited by Walter Senn et al., Springer
Nature Switzerland, 2026, pp. 442–454, doi:10.1007/978-3-032-04558-4_35.
short: 'S.S. Hotegni, 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. 442–454.'
date_created: 2026-10-01T11:53:11Z
date_updated: 2026-10-01T11:53:46Z
department:
- _id: '655'
doi: 10.1007/978-3-032-04558-4_35
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: 442–454
place: Cham
publication: Artificial Neural Networks and Machine Learning – ICANN 2025
publication_identifier:
isbn:
- 978-3-032-04558-4
publisher: Springer Nature Switzerland
status: public
title: Enhancing Adversarial Robustness Through Multi-objective Representation Learning
type: conference
user_id: '47427'
year: '2026'
...
---
_id: '67302'
abstract:
- lang: eng
text: Data-driven surrogate models provide fast and fully differentiable approximations
of complex dynamical systems. In this work, we develop such surrogates for the
Rayleigh–Bénard convection (RBC), which governs thermally driven flows in natural
and industrial environments. Specifically, the proposed models approximate the
discrete-time flow map of the RBC system, advancing the full system state by a
fixed time step. We train Fourier Neural Operator (FNO)–based models to learn
the dynamics of RBC in two and three dimensions and compare them to a convolutional
U-Net baseline and a Koopman-based Linear Recurrent Autoencoder Network (LRAN).
The two-dimensional system serves as a baseline for the more challenging three-dimensional
case, which exhibits increased spatial complexity and turbulent dynamics. Across
all settings, FNO-based models consistently outperform the LRAN, while achieving
performance comparable to the U-Net in several regimes. Incorporating spatio-temporal
inputs via FNOs leads to improved long-term prediction accuracy, particularly
for turbulent flows. The physical fidelity of the predictions is assessed using
convective heat flux statistics, profiles, and fluctuations, showing that FNOs
most closely reproduce the ground-truth flow statistics. In addition, we demonstrate
that FNOs enable zero-shot super-resolution across unseen spatial discretizations,
a capability not shared by the convolutional baselines. These results highlight
the potential of neural operator–based models as accurate, physically consistent,
and resolution-independent surrogates for downstream tasks such as flow control.
author:
- first_name: Thorben
full_name: Markmann, Thorben
last_name: Markmann
- first_name: Michiel
full_name: Straat, Michiel
last_name: Straat
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
citation:
ama: Markmann T, Straat M, Peitz S, Hammer B. Fourier neural operators as data-driven
surrogates for two- and three-dimensional Rayleigh–Bénard convection. Neurocomputing.
2026;679:133201. doi:10.1016/j.neucom.2026.133201
apa: Markmann, T., Straat, M., Peitz, S., & Hammer, B. (2026). Fourier neural
operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard
convection. Neurocomputing, 679, 133201. https://doi.org/10.1016/j.neucom.2026.133201
bibtex: '@article{Markmann_Straat_Peitz_Hammer_2026, title={Fourier neural operators
as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection},
volume={679}, DOI={10.1016/j.neucom.2026.133201},
journal={Neurocomputing}, author={Markmann, Thorben and Straat, Michiel and Peitz,
Sebastian and Hammer, Barbara}, year={2026}, pages={133201} }'
chicago: 'Markmann, Thorben, Michiel Straat, Sebastian Peitz, and Barbara Hammer.
“Fourier Neural Operators as Data-Driven Surrogates for Two- and Three-Dimensional
Rayleigh–Bénard Convection.” Neurocomputing 679 (2026): 133201. https://doi.org/10.1016/j.neucom.2026.133201.'
ieee: 'T. Markmann, M. Straat, S. Peitz, and B. Hammer, “Fourier neural operators
as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection,”
Neurocomputing, vol. 679, p. 133201, 2026, doi: 10.1016/j.neucom.2026.133201.'
mla: Markmann, Thorben, et al. “Fourier Neural Operators as Data-Driven Surrogates
for Two- and Three-Dimensional Rayleigh–Bénard Convection.” Neurocomputing,
vol. 679, 2026, p. 133201, doi:10.1016/j.neucom.2026.133201.
short: T. Markmann, M. Straat, S. Peitz, B. Hammer, Neurocomputing 679 (2026) 133201.
date_created: 2026-10-01T11:55:20Z
date_updated: 2026-10-01T11:56:04Z
department:
- _id: '655'
doi: 10.1016/j.neucom.2026.133201
intvolume: ' 679'
keyword:
- own
- own-journal
- erc
language:
- iso: eng
page: '133201'
publication: Neurocomputing
publication_identifier:
issn:
- 0925-2312
status: public
title: Fourier neural operators as data-driven surrogates for two- and three-dimensional
Rayleigh–Bénard convection
type: journal_article
user_id: '47427'
volume: 679
year: '2026'
...
---
_id: '67299'
author:
- first_name: Hans
full_name: Harder, Hans
id: '98879'
last_name: Harder
- first_name: Abhijeet
full_name: Vishwasrao, Abhijeet
last_name: Vishwasrao
- first_name: Luca
full_name: Guastoni, Luca
last_name: Guastoni
- first_name: Ricardo
full_name: Vinuesa, Ricardo
last_name: Vinuesa
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
citation:
ama: 'Harder H, Vishwasrao A, Guastoni L, Vinuesa R, Peitz S. Efficient probabilistic
surrogate modeling techniques for partially-observed large-scale dynamical systems.
In: Sukhatme G, Lindemann L, Tu S, Wierman A, Atanasov N, eds. Proceedings
of The 8th Annual Learning for Dynamics and Control Conference. Vol 331. Proceedings
of Machine Learning Research. PMLR; 2026:1601–1619. doi:10.48550/arXiv.2511.04641'
apa: Harder, H., Vishwasrao, A., Guastoni, L., Vinuesa, R., & Peitz, S. (2026).
Efficient probabilistic surrogate modeling techniques for partially-observed large-scale
dynamical systems. In G. Sukhatme, L. Lindemann, S. Tu, A. Wierman, & N. Atanasov
(Eds.), Proceedings of The 8th Annual Learning for Dynamics and Control Conference
(Vol. 331, pp. 1601–1619). PMLR. https://doi.org/10.48550/arXiv.2511.04641
bibtex: '@inproceedings{Harder_Vishwasrao_Guastoni_Vinuesa_Peitz_2026, series={Proceedings
of Machine Learning Research}, title={Efficient probabilistic surrogate modeling
techniques for partially-observed large-scale dynamical systems}, volume={331},
DOI={10.48550/arXiv.2511.04641},
booktitle={Proceedings of The 8th Annual Learning for Dynamics and Control Conference},
publisher={PMLR}, author={Harder, Hans and Vishwasrao, Abhijeet and Guastoni,
Luca and Vinuesa, Ricardo and Peitz, Sebastian}, editor={Sukhatme, Gaurav and
Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, year={2026},
pages={1601–1619}, collection={Proceedings of Machine Learning Research} }'
chicago: Harder, Hans, Abhijeet Vishwasrao, Luca Guastoni, Ricardo Vinuesa, and
Sebastian Peitz. “Efficient Probabilistic Surrogate Modeling Techniques for Partially-Observed
Large-Scale Dynamical Systems.” In Proceedings of The 8th Annual Learning for
Dynamics and Control Conference, edited by Gaurav Sukhatme, Lars Lindemann,
Stephen Tu, Adam Wierman, and Nikolay Atanasov, 331:1601–1619. Proceedings of
Machine Learning Research. PMLR, 2026. https://doi.org/10.48550/arXiv.2511.04641.
ieee: 'H. Harder, A. Vishwasrao, L. Guastoni, R. Vinuesa, and S. Peitz, “Efficient
probabilistic surrogate modeling techniques for partially-observed large-scale
dynamical systems,” in Proceedings of The 8th Annual Learning for Dynamics
and Control Conference, 2026, vol. 331, pp. 1601–1619, doi: 10.48550/arXiv.2511.04641.'
mla: Harder, Hans, et al. “Efficient Probabilistic Surrogate Modeling Techniques
for Partially-Observed Large-Scale Dynamical Systems.” Proceedings of The 8th
Annual Learning for Dynamics and Control Conference, edited by Gaurav Sukhatme
et al., vol. 331, PMLR, 2026, pp. 1601–1619, doi:10.48550/arXiv.2511.04641.
short: 'H. Harder, A. Vishwasrao, L. Guastoni, R. Vinuesa, S. Peitz, in: G. Sukhatme,
L. Lindemann, S. Tu, A. Wierman, N. Atanasov (Eds.), Proceedings of The 8th Annual
Learning for Dynamics and Control Conference, PMLR, 2026, pp. 1601–1619.'
date_created: 2026-10-01T11:51:57Z
date_updated: 2026-10-01T11:52:48Z
department:
- _id: '655'
doi: 10.48550/arXiv.2511.04641
editor:
- first_name: Gaurav
full_name: Sukhatme, Gaurav
last_name: Sukhatme
- first_name: Lars
full_name: Lindemann, Lars
last_name: Lindemann
- first_name: Stephen
full_name: Tu, Stephen
last_name: Tu
- first_name: Adam
full_name: Wierman, Adam
last_name: Wierman
- first_name: Nikolay
full_name: Atanasov, Nikolay
last_name: Atanasov
intvolume: ' 331'
keyword:
- own
- own-conference
- erc
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/pdf?id=Z9srtyqVLE
oa: '1'
page: 1601–1619
publication: Proceedings of The 8th Annual Learning for Dynamics and Control Conference
publisher: PMLR
series_title: Proceedings of Machine Learning Research
status: public
title: Efficient probabilistic surrogate modeling techniques for partially-observed
large-scale dynamical systems
type: conference
user_id: '47427'
volume: 331
year: '2026'
...
---
_id: '67303'
author:
- first_name: Augustina Chidinma
full_name: Amakor, Augustina Chidinma
id: '97916'
last_name: Amakor
- first_name: Konstantin
full_name: Sonntag, Konstantin
id: '56399'
last_name: Sonntag
orcid: https://orcid.org/0000-0003-3384-3496
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
citation:
ama: 'Amakor AC, Sonntag K, Peitz S. Interactive Pareto navigation for deep multi-task
learning. In: European Conference on Machine Learning and Principles and Practice
of Knowledge Discovery in Databases (ECML PKDD). ; 2026:652-668. doi:10.1007/978-3-032-37667-1_37'
apa: Amakor, A. C., Sonntag, K., & Peitz, S. (2026). Interactive Pareto navigation
for deep multi-task learning. European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECML PKDD), 652–668. https://doi.org/10.1007/978-3-032-37667-1_37
bibtex: '@inproceedings{Amakor_Sonntag_Peitz_2026, title={Interactive Pareto navigation
for deep multi-task learning}, DOI={10.1007/978-3-032-37667-1_37},
booktitle={European Conference on Machine Learning and Principles and Practice
of Knowledge Discovery in Databases (ECML PKDD)}, author={Amakor, Augustina Chidinma
and Sonntag, Konstantin and Peitz, Sebastian}, year={2026}, pages={652–668} }'
chicago: Amakor, Augustina Chidinma, Konstantin Sonntag, and Sebastian Peitz. “Interactive
Pareto Navigation for Deep Multi-Task Learning.” In European Conference on
Machine Learning and Principles and Practice of Knowledge Discovery in Databases
(ECML PKDD), 652–68, 2026. https://doi.org/10.1007/978-3-032-37667-1_37.
ieee: 'A. C. Amakor, K. Sonntag, and S. Peitz, “Interactive Pareto navigation for
deep multi-task learning,” in European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECML PKDD), 2026, pp. 652–668,
doi: 10.1007/978-3-032-37667-1_37.'
mla: Amakor, Augustina Chidinma, et al. “Interactive Pareto Navigation for Deep
Multi-Task Learning.” European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECML PKDD), 2026, pp. 652–68,
doi:10.1007/978-3-032-37667-1_37.
short: 'A.C. Amakor, K. Sonntag, S. Peitz, in: European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2026,
pp. 652–668.'
date_created: 2026-10-01T11:56:15Z
date_updated: 2026-10-01T11:58:43Z
department:
- _id: '655'
doi: 10.1007/978-3-032-37667-1_37
keyword:
- own
- own-conference
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2606.19521
oa: '1'
page: 652-668
publication: European Conference on Machine Learning and Principles and Practice of
Knowledge Discovery in Databases (ECML PKDD)
status: public
title: Interactive Pareto navigation for deep multi-task learning
type: conference
user_id: '47427'
year: '2026'
...
---
_id: '67305'
author:
- first_name: Jannis
full_name: Becktepe, Jannis
last_name: Becktepe
- first_name: Aleksandra
full_name: Franz, Aleksandra
last_name: Franz
- first_name: Nils
full_name: Thuerey, Nils
last_name: Thuerey
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
citation:
ama: 'Becktepe J, Franz A, Thuerey N, Peitz S. Plug-and-Play Benchmarking of Reinforcement
Learning Algorithms for Large-Scale Flow Control. In: International Conference
on Machine Learning (ICML). ; 2026. doi:10.48550/arXiv.2601.15015'
apa: Becktepe, J., Franz, A., Thuerey, N., & Peitz, S. (2026). Plug-and-Play
Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control.
International Conference on Machine Learning (ICML). https://doi.org/10.48550/arXiv.2601.15015
bibtex: '@inproceedings{Becktepe_Franz_Thuerey_Peitz_2026, title={Plug-and-Play
Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control},
DOI={10.48550/arXiv.2601.15015},
booktitle={International Conference on Machine Learning (ICML)}, author={Becktepe,
Jannis and Franz, Aleksandra and Thuerey, Nils and Peitz, Sebastian}, year={2026}
}'
chicago: Becktepe, Jannis, Aleksandra Franz, Nils Thuerey, and Sebastian Peitz.
“Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale
Flow Control.” In International Conference on Machine Learning (ICML),
2026. https://doi.org/10.48550/arXiv.2601.15015.
ieee: 'J. Becktepe, A. Franz, N. Thuerey, and S. Peitz, “Plug-and-Play Benchmarking
of Reinforcement Learning Algorithms for Large-Scale Flow Control,” 2026, doi:
10.48550/arXiv.2601.15015.'
mla: Becktepe, Jannis, et al. “Plug-and-Play Benchmarking of Reinforcement Learning
Algorithms for Large-Scale Flow Control.” International Conference on Machine
Learning (ICML), 2026, doi:10.48550/arXiv.2601.15015.
short: 'J. Becktepe, A. Franz, N. Thuerey, S. Peitz, in: International Conference
on Machine Learning (ICML), 2026.'
date_created: 2026-10-01T11:59:35Z
date_updated: 2026-10-01T12:00:51Z
department:
- _id: '655'
doi: 10.48550/arXiv.2601.15015
keyword:
- own
- own-conference
- erc
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://safe-autonomous-systems.github.io/fluidgym/
oa: '1'
publication: International Conference on Machine Learning (ICML)
status: public
title: Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale
Flow Control
type: conference
user_id: '47427'
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: '67298'
author:
- first_name: Christian
full_name: Mugisho Zagabe, Christian
last_name: Mugisho Zagabe
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
citation:
ama: Mugisho Zagabe C, Peitz S. Automatic feature identification in least-squares
policy iteration using the Koopman operator framework. arXiv:260326464.
Published online 2026.
apa: Mugisho Zagabe, C., & Peitz, S. (2026). Automatic feature identification
in least-squares policy iteration using the Koopman operator framework. In arXiv:2603.26464.
bibtex: '@article{Mugisho Zagabe_Peitz_2026, title={Automatic feature identification
in least-squares policy iteration using the Koopman operator framework}, journal={arXiv:2603.26464},
author={Mugisho Zagabe, Christian and Peitz, Sebastian}, year={2026} }'
chicago: Mugisho Zagabe, Christian, and Sebastian Peitz. “Automatic Feature Identification
in Least-Squares Policy Iteration Using the Koopman Operator Framework.” ArXiv:2603.26464,
2026.
ieee: C. Mugisho Zagabe and S. Peitz, “Automatic feature identification in least-squares
policy iteration using the Koopman operator framework,” arXiv:2603.26464.
2026.
mla: Mugisho Zagabe, Christian, and Sebastian Peitz. “Automatic Feature Identification
in Least-Squares Policy Iteration Using the Koopman Operator Framework.” ArXiv:2603.26464,
2026.
short: C. Mugisho Zagabe, S. Peitz, ArXiv:2603.26464 (2026).
date_created: 2026-10-01T11:50:28Z
date_updated: 2026-10-01T11:51:41Z
department:
- _id: '655'
keyword:
- own
- own-preprint
- erc
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2603.26464
oa: '1'
publication: arXiv:2603.26464
status: public
title: Automatic feature identification in least-squares policy iteration using the
Koopman operator framework
type: preprint
user_id: '47427'
year: '2026'
...
---
_id: '67304'
author:
- first_name: Tim
full_name: Plotzki, Tim
last_name: Plotzki
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
citation:
ama: Plotzki T, Peitz S. Koopman-based surrogate modeling for reinforcement-learning-control
of Rayleigh-Benard convection. arXiv:260328074. Published online 2026.
apa: Plotzki, T., & Peitz, S. (2026). Koopman-based surrogate modeling for reinforcement-learning-control
of Rayleigh-Benard convection. In arXiv:2603.28074.
bibtex: '@article{Plotzki_Peitz_2026, title={Koopman-based surrogate modeling for
reinforcement-learning-control of Rayleigh-Benard convection}, journal={arXiv:2603.28074},
author={Plotzki, Tim and Peitz, Sebastian}, year={2026} }'
chicago: Plotzki, Tim, and Sebastian Peitz. “Koopman-Based Surrogate Modeling for
Reinforcement-Learning-Control of Rayleigh-Benard Convection.” ArXiv:2603.28074,
2026.
ieee: T. Plotzki and S. Peitz, “Koopman-based surrogate modeling for reinforcement-learning-control
of Rayleigh-Benard convection,” arXiv:2603.28074. 2026.
mla: Plotzki, Tim, and Sebastian Peitz. “Koopman-Based Surrogate Modeling for Reinforcement-Learning-Control
of Rayleigh-Benard Convection.” ArXiv:2603.28074, 2026.
short: T. Plotzki, S. Peitz, ArXiv:2603.28074 (2026).
date_created: 2026-10-01T11:59:03Z
date_updated: 2026-10-01T11:59:27Z
department:
- _id: '655'
keyword:
- own
- own-preprint
- erc
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2603.28074
oa: '1'
publication: arXiv:2603.28074
status: public
title: Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard
convection
type: preprint
user_id: '47427'
year: '2026'
...
---
_id: '62816'
abstract:
- lang: eng
text: The increasing demand for advanced sensing technologies drives the development
of chemical sensors using innovative materials. In gas sensing, optical sensors
are often used to detect gases such as CO, NOx, and O2. Oxygen sensors typically
incorporate dyes into oxygen-permeable matrices like polymers, silica, or zeolites.
Alternatively, semiconductor surface chemistry can enable O2 detection. However,
these approaches are often limited by slow response and recovery times and low
selectivity, restricting their practical applications. The metal-organic framework
MOF-76(Eu) and its yttrium-modified variant, MOF-76(Eu/Y) are reported to exhibit
highly reversible and fast optical responses to varying O2 concentrations. Time-resolved
emission measurements are performed over short (seconds) and long (hours) timescales
using N2 and synthetic air mixtures. Cross-sensitivity to humidity is analyzed.
Multichannel scaling photon-counting experiments confirm quenching at the linker
level, as the emission lifetime remains nearly constant. Yttrium significantly
improves stability and performance at room temperature. Structural and optical
changes induced by yttrium are investigated. Additionally, MIL-78(Eu), another
Eu-BTC-based MOF with a different coordination environment, is synthesized. Unlike
MOF-76(Eu), MIL-78(Eu) exhibits distinct optical properties but lacks a reversible
response to O2. These results highlight the potential of MOF-76-based materials
for high-performance O2 sensing.
article_number: e11190
author:
- first_name: Zhenyu
full_name: Zhao, Zhenyu
last_name: Zhao
- first_name: Christian
full_name: Weinberger, Christian
id: '11848'
last_name: Weinberger
- first_name: Jakob
full_name: Steube, Jakob
id: '40342'
last_name: Steube
orcid: 0000-0003-3178-4429
- first_name: Matthias
full_name: Bauer, Matthias
id: '47241'
last_name: Bauer
orcid: 0000-0002-9294-6076
- first_name: Martin
full_name: Brehm, Martin
id: '100167'
last_name: Brehm
- first_name: Michael
full_name: Tiemann, Michael
id: '23547'
last_name: Tiemann
orcid: 0000-0003-1711-2722
citation:
ama: Zhao Z, Weinberger C, Steube J, Bauer M, Brehm M, Tiemann M. Fast‐Responding
O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76).
Advanced Functional Materials. Published online 2026. doi:10.1002/adfm.202511190
apa: Zhao, Z., Weinberger, C., Steube, J., Bauer, M., Brehm, M., & Tiemann,
M. (2026). Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic
Frameworks (MOF‐76). Advanced Functional Materials, Article e11190. https://doi.org/10.1002/adfm.202511190
bibtex: '@article{Zhao_Weinberger_Steube_Bauer_Brehm_Tiemann_2026, title={Fast‐Responding
O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76)},
DOI={10.1002/adfm.202511190},
number={e11190}, journal={Advanced Functional Materials}, publisher={Wiley}, author={Zhao,
Zhenyu and Weinberger, Christian and Steube, Jakob and Bauer, Matthias and Brehm,
Martin and Tiemann, Michael}, year={2026} }'
chicago: Zhao, Zhenyu, Christian Weinberger, Jakob Steube, Matthias Bauer, Martin
Brehm, and Michael Tiemann. “Fast‐Responding O2 Gas Sensor Based on Luminescent
Europium Metal‐Organic Frameworks (MOF‐76).” Advanced Functional Materials,
2026. https://doi.org/10.1002/adfm.202511190.
ieee: 'Z. Zhao, C. Weinberger, J. Steube, M. Bauer, M. Brehm, and M. Tiemann, “Fast‐Responding
O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76),”
Advanced Functional Materials, Art. no. e11190, 2026, doi: 10.1002/adfm.202511190.'
mla: Zhao, Zhenyu, et al. “Fast‐Responding O2 Gas Sensor Based on Luminescent Europium
Metal‐Organic Frameworks (MOF‐76).” Advanced Functional Materials, e11190,
Wiley, 2026, doi:10.1002/adfm.202511190.
short: Z. Zhao, C. Weinberger, J. Steube, M. Bauer, M. Brehm, M. Tiemann, Advanced
Functional Materials (2026).
date_created: 2025-12-03T17:09:28Z
date_updated: 2026-10-01T14:06:35Z
department:
- _id: '35'
- _id: '2'
- _id: '307'
doi: 10.1002/adfm.202511190
language:
- iso: eng
main_file_link:
- open_access: '1'
oa: '1'
publication: Advanced Functional Materials
publication_identifier:
issn:
- 1616-301X
- 1616-3028
publication_status: published
publisher: Wiley
quality_controlled: '1'
status: public
title: Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks
(MOF‐76)
type: journal_article
user_id: '23547'
year: '2026'
...
---
_id: '67339'
abstract:
- lang: eng
text: Explainable artificial intelligence (XAI) is essential for critical domains
such as healthcare and autonomous systems to build trust and confidence in real-world
deployment. In this context, description logic knowledge bases (KBs) provide structured
and semantically rich representations that support reasoning and informed decision-making.
A core task in applying KBs to XAI is class expression learning (CEL), which generates
explainable logical descriptions for classifying instances within KBs. Unlike
black-box models with opaque internal mechanisms, CEL provides global explainability
and ease of integration with domain knowledge. However, current approaches to
CEL face significant limitations such as poor scalability, failure to capture
rare patterns, and limited exploration of the vast class expression search space.
To overcome these limitations, we introduce LYRA, a novel multi-agent deep reinforcement
learning framework that formulates CEL as a collaborative planning task under
uncertainty. The integration of the Dempster–Shafer theory enables agents to effectively
reason under ambiguity and manage conflicting or inconsistent information. Our
experiments show that LYRA outperforms state-of-the-art methods on seven out of
eight datasets, demonstrating robust and scalable CEL. Additionally, LYRA offers
interpretable decisions and employs advanced search strategies, enabling the discovery
of more precise and expressive class expressions than existing approaches.
author:
- first_name: Amgad
full_name: Abdulmaqsod, Amgad
last_name: Abdulmaqsod
- first_name: Yasir
full_name: Mahmood, Yasir
last_name: Mahmood
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
last_name: Ngonga Ngomo
- first_name: Mohamed Ahmed
full_name: Sherif, Mohamed Ahmed
last_name: Sherif
citation:
ama: 'Abdulmaqsod A, Mahmood Y, Ngonga Ngomo A-C, Sherif MA. LYRA: Belief-Driven
Scalable Class Expression Learning in Description Logics. In: The Semantic
Web – ISWC 2026. ; 2026.'
apa: 'Abdulmaqsod, A., Mahmood, Y., Ngonga Ngomo, A.-C., & Sherif, M. A. (2026).
LYRA: Belief-Driven Scalable Class Expression Learning in Description Logics.
The Semantic Web – ISWC 2026.'
bibtex: '@inproceedings{Abdulmaqsod_Mahmood_Ngonga Ngomo_Sherif_2026, place={Bari,
Italy}, title={LYRA: Belief-Driven Scalable Class Expression Learning in Description
Logics}, booktitle={The Semantic Web – ISWC 2026}, author={Abdulmaqsod, Amgad
and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed Ahmed},
year={2026} }'
chicago: 'Abdulmaqsod, Amgad, Yasir Mahmood, Axel-Cyrille Ngonga Ngomo, and Mohamed
Ahmed Sherif. “LYRA: Belief-Driven Scalable Class Expression Learning in Description
Logics.” In The Semantic Web – ISWC 2026. Bari, Italy, 2026.'
ieee: 'A. Abdulmaqsod, Y. Mahmood, A.-C. Ngonga Ngomo, and M. A. Sherif, “LYRA:
Belief-Driven Scalable Class Expression Learning in Description Logics,” 2026.'
mla: 'Abdulmaqsod, Amgad, et al. “LYRA: Belief-Driven Scalable Class Expression
Learning in Description Logics.” The Semantic Web – ISWC 2026, 2026.'
short: 'A. Abdulmaqsod, Y. Mahmood, A.-C. Ngonga Ngomo, M.A. Sherif, in: The Semantic
Web – ISWC 2026, Bari, Italy, 2026.'
date_created: 2026-10-02T07:50:24Z
date_updated: 2026-10-02T08:00:46Z
keyword:
- amgad dice enexa fairomics mahmood ngonga sailproject sherif simba whale
place: Bari, Italy
publication: The Semantic Web – ISWC 2026
status: public
title: 'LYRA: Belief-Driven Scalable Class Expression Learning in Description Logics'
type: conference
user_id: '67234'
year: '2026'
...
---
_id: '67344'
author:
- first_name: Alexander
full_name: Becker, Alexander
last_name: Becker
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
citation:
ama: 'Becker A, Ngonga Ngomo A-C, Sherif M. TIM: Tiered Iterative Knowledge Graph
Matching. In: The Semantic Web – 23rd European Semantic Web Conference, ESWC
2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings. Springer Nature Switzerland;
2026.'
apa: 'Becker, A., Ngonga Ngomo, A.-C., & Sherif, M. (2026). TIM: Tiered Iterative
Knowledge Graph Matching. The Semantic Web – 23rd European Semantic Web Conference,
ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings.'
bibtex: '@inproceedings{Becker_Ngonga Ngomo_Sherif_2026, title={TIM: Tiered Iterative
Knowledge Graph Matching}, booktitle={The Semantic Web – 23rd European Semantic
Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings},
publisher={Springer Nature Switzerland}, author={Becker, Alexander and Ngonga
Ngomo, Axel-Cyrille and Sherif, Mohamed}, year={2026} }'
chicago: 'Becker, Alexander, Axel-Cyrille Ngonga Ngomo, and Mohamed Sherif. “TIM:
Tiered Iterative Knowledge Graph Matching.” In The Semantic Web – 23rd European
Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings.
Springer Nature Switzerland, 2026.'
ieee: 'A. Becker, A.-C. Ngonga Ngomo, and M. Sherif, “TIM: Tiered Iterative Knowledge
Graph Matching,” 2026.'
mla: 'Becker, Alexander, et al. “TIM: Tiered Iterative Knowledge Graph Matching.”
The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik
, Croatia, May 10-14, 2026, Proceedings, Springer Nature Switzerland, 2026.'
short: 'A. Becker, A.-C. Ngonga Ngomo, M. Sherif, in: The Semantic Web – 23rd European
Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings,
Springer Nature Switzerland, 2026.'
date_created: 2026-10-02T07:57:09Z
date_updated: 2026-10-02T07:57:39Z
keyword:
- becker dice enexa kiowl ngonga sailproject sherif trr318_inf whale
language:
- iso: eng
publication: The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026,
Dubrovnik , Croatia, May 10-14, 2026, Proceedings
publisher: Springer Nature Switzerland
status: public
title: 'TIM: Tiered Iterative Knowledge Graph Matching'
type: conference
user_id: '67234'
year: '2026'
...
---
_id: '67341'
author:
- first_name: Duygu
full_name: Ekinci Birol, Duygu
last_name: Ekinci Birol
- first_name: N'Dah Jean
full_name: KOUAGOU, N'Dah Jean
id: '87189'
last_name: KOUAGOU
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Ekinci Birol D, KOUAGOU NJ, Sherif M, Ngonga Ngomo A-C. ATLAS: Adaptive Attribute-Aware
Post-Hoc Alignment of Knowledge Graph Embeddings. In: IEEE International Conference
on Data Mining (ICDM) 2026. ; 2026.'
apa: 'Ekinci Birol, D., KOUAGOU, N. J., Sherif, M., & Ngonga Ngomo, A.-C. (2026).
ATLAS: Adaptive Attribute-Aware Post-Hoc Alignment of Knowledge Graph Embeddings.
IEEE International Conference on Data Mining (ICDM) 2026.'
bibtex: '@inproceedings{Ekinci Birol_KOUAGOU_Sherif_Ngonga Ngomo_2026, title={ATLAS:
Adaptive Attribute-Aware Post-Hoc Alignment of Knowledge Graph Embeddings}, booktitle={IEEE
International Conference on Data Mining (ICDM) 2026}, author={Ekinci Birol, Duygu
and KOUAGOU, N’Dah Jean and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2026}
}'
chicago: 'Ekinci Birol, Duygu, N’Dah Jean KOUAGOU, Mohamed Sherif, and Axel-Cyrille
Ngonga Ngomo. “ATLAS: Adaptive Attribute-Aware Post-Hoc Alignment of Knowledge
Graph Embeddings.” In IEEE International Conference on Data Mining (ICDM) 2026,
2026.'
ieee: 'D. Ekinci Birol, N. J. KOUAGOU, M. Sherif, and A.-C. Ngonga Ngomo, “ATLAS:
Adaptive Attribute-Aware Post-Hoc Alignment of Knowledge Graph Embeddings,” 2026.'
mla: 'Ekinci Birol, Duygu, et al. “ATLAS: Adaptive Attribute-Aware Post-Hoc Alignment
of Knowledge Graph Embeddings.” IEEE International Conference on Data Mining
(ICDM) 2026, 2026.'
short: 'D. Ekinci Birol, N.J. KOUAGOU, M. Sherif, A.-C. Ngonga Ngomo, in: IEEE International
Conference on Data Mining (ICDM) 2026, 2026.'
date_created: 2026-10-02T07:51:10Z
date_updated: 2026-10-02T08:02:17Z
keyword:
- dice duygu fairomics kouagou ngonga sail sherif trr318 whale
language:
- iso: eng
publication: IEEE International Conference on Data Mining (ICDM) 2026
status: public
title: 'ATLAS: Adaptive Attribute-Aware Post-Hoc Alignment of Knowledge Graph Embeddings'
type: conference
user_id: '67234'
year: '2026'
...