@inproceedings{26539,
  abstract     = {{In control design most control strategies are model-based and require accurate models to be applied successfully. Due to simplifications and the model-reality-gap physics-derived models frequently exhibit deviations from real-world-systems. Likewise, purely data-driven methods often do not generalise well enough and may violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired loss functions separately have shown promising results to conquer these drawbacks. In this contribution we extend existing methods towards the identification of non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN and a physics-inspired loss term (-L) to successfully identify the system's dynamics, while maintaining the consistency with physical laws. The proposed method is demonstrated on two real-world nonlinear systems and outperforms existing techniques regarding complexity and reliability.}},
  author       = {{Götte, Ricarda-Samantha and Timmermann, Julia}},
  booktitle    = {{2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)}},
  keywords     = {{data-driven, physics-based, physics-informed, neural networks, system identification, hybrid modelling}},
  location     = {{Cairo, Egypt}},
  pages        = {{67--76}},
  title        = {{{Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering}}},
  doi          = {{10.1109/AIRC56195.2022.9836982}},
  year         = {{2022}},
}

@inproceedings{31066,
  abstract     = {{While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. }},
  author       = {{Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}},
  booktitle    = {{14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}},
  keywords     = {{neural networks, physics-guided, data-driven, multi-objective optimization, system identification, machine learning, dynamical systems}},
  location     = {{Casablanca, Morocco}},
  number       = {{12}},
  pages        = {{19--24}},
  title        = {{{Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}}},
  doi          = {{https://doi.org/10.1016/j.ifacol.2022.07.282}},
  volume       = {{55}},
  year         = {{2022}},
}

@inproceedings{29803,
  abstract     = {{Ultrasonic wire bonding is a solid-state joining process used to form electrical interconnections in micro and
power electronics and batteries. A high frequency oscillation causes a metallurgical bond deformation in
the contact area. Due to the numerous physical influencing factors, it is very difficult to accurately capture
this process in a model. Therefore, our goal is to determine a suitable feed-forward control strategy for the
bonding process even without detailed model knowledge. We propose the use of batch constrained Bayesian
optimization for the control design. Hence, Bayesian optimization is precisely adapted to the application of
bonding: the constraint is used to check one quality feature of the process and the use of batches leads to
more efficient experiments. Our approach is suitable to determine a feed-forward control for the bonding
process that provides very high quality bonds without using a physical model. We also show that the quality
of the Bayesian optimization based control outperforms random search as well as manual search by a user.
Using a simple prior knowledge model derived from data further improves the quality of the connection.
The Bayesian optimization approach offers the possibility to perform a sensitivity analysis of the control
parameters, which allows to evaluate the influence of each control parameter on the bond quality. In summary,
Bayesian optimization applied to the bonding process provides an excellent opportunity to develop a feedforward
control without full modeling of the underlying physical processes.}},
  author       = {{Hesse, Michael and Hunstig, Matthias and Timmermann, Julia and Trächtler, Ansgar}},
  booktitle    = {{Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)}},
  isbn         = {{978-989-758-549-4}},
  keywords     = {{Bayesian optimization, Wire bonding, Feed-forward control, model-free design}},
  location     = {{Online}},
  pages        = {{383--394}},
  title        = {{{Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design}}},
  year         = {{2022}},
}

@inbook{57037,
  abstract     = {{Es wird zur Diskussion gestellt, ob Sozialplanung, aber auch Soziale Arbeit, eine naiv-positive Auffassung vom Begriff „Netzwerk“ haben. Der Begriff ist trügerisch, da er einerseits analytisch-begrifflich nicht trennscharf zu bestimmen ist. Andererseits sind mit dem Bild des Netzwerks normative Aussagen impliziert, die in hohem Maße widersprüchlich sind. Bezieht sich das Bild des Netzwerks nicht auf Sachverhalte und Phänomene, die mit anderen Begrifflichkeiten exakter, weil theoretisch und empirisch verweisungsstärker, zu bestimmen wären?}},
  author       = {{Ristau, Alexander and Krüger, Tim and Winkler, Michael}},
  booktitle    = {{Schlüsselbegriffe der Sozialplanung und ihre Kritik}},
  editor       = {{Rund, Mario and Peters, Friedhelm}},
  isbn         = {{978-3-658-38398-5}},
  pages        = {{129--138}},
  publisher    = {{Springer VS }},
  title        = {{{Netzwerke}}},
  doi          = {{10.1007/978-3-658-38399-2}},
  year         = {{2022}},
}

@article{57036,
  abstract     = {{In diesem Artikel wird in Bezug auf Fragen des Verstehens diskutiert,
ob es der Sozialen Arbeit nicht guttäte, sich von anderen
Quellen als den eigenen Theorien inspirieren, vielleicht sogar provozieren
zu lassen. Dazu wird zunächst das Verhältnis zwischen Sozialer
Arbeit und Belletristik, der ›schönen Literatur‹, in den Blick
genommen.


}},
  author       = {{Ristau, Alexander}},
  issn         = {{0340-8469}},
  journal      = {{Sozialmagazin}},
  number       = {{8}},
  pages        = {{61--77}},
  publisher    = {{Beltz}},
  title        = {{{Soziale Arbeit über Belletristik verstehen}}},
  doi          = {{10.3262/SM2208061}},
  year         = {{2022}},
}

@article{49461,
  author       = {{Trang, Simon Thanh-Nam and Mandrella, M. and Marrone, M. and Kolbe, L.}},
  journal      = {{European Journal of Information Systems (VHB Jourqual 3 A)}},
  pages        = {{166--187}},
  title        = {{{Co-creating business value through IT-business operational alignment in inter-organisational relationships: Empirical evidence from regional networks}}},
  volume       = {{31}},
  year         = {{2022}},
}

@inbook{32417,
  author       = {{Tönsing, Johanna}},
  booktitle    = {{Interpretationsverfahruen der germanistischen Literaturdidaktik und didaktische Referenzkonzepte}},
  editor       = {{Bernhardt, Sebastian and Hardtke, Thomas}},
  title        = {{{(K)eine kinderleichte Gattung:  Konsequenzen einer kulturwissenschaftlich informierten Märchendidaktik}}},
  year         = {{2022}},
}

@unpublished{58185,
  abstract     = {{We consider a variant of the ring of components of Hurwitz spaces introduced
by Ellenberg, Venkatesh and Westerland. By focusing on Hurwitz spaces
classifying covers of the projective line, the resulting ring of components is
commutative, which lets us study it from the point of view of algebraic
geometry and relate its geometric properties to numerical invariants involved
in our previously obtained asymptotic counts. Specifically, we describe a
stratification of the prime spectrum of the ring of components, and we compute
the dimensions and degrees of the strata. Using the stratification, we give a
complete description of the spectrum in some cases.}},
  author       = {{Seguin, Beranger Fabrice}},
  booktitle    = {{arXiv:2210.12793}},
  title        = {{{The Geometry of Rings of Components of Hurwitz Spaces}}},
  year         = {{2022}},
}

@article{58201,
  author       = {{Beutner, Marc and Grüttner, Niclas Christian}},
  journal      = {{Kölner Zeitschrift für Wirtschaft und Pädagogik.}},
  number       = {{73}},
  pages        = {{69--104}},
  publisher    = {{Kölner Arbeitskreis Wirtschaft/Pädagogik}},
  title        = {{{Konsumpädagogik und nachhaltige Bildung – Ein Spannungsfeld zwischen Bildung, Ökonomie, Nachhaligkeit und das Recht auf eine persönliche Entwicklung. }}},
  volume       = {{38}},
  year         = {{2022}},
}

@article{48780,
  abstract     = {{Explainable Artificial Intelligence (XAI) has mainly focused on static learning tasks so far. In this paper, we consider XAI in the context of online learning in dynamic environments, such as learning from real-time data streams, where models are learned incrementally and continuously adapted over the course of time. More specifically, we motivate the problem of explaining model change, i.e. explaining the difference between models before and after adaptation, instead of the models themselves. In this regard, we provide the first efficient model-agnostic approach to dynamically detecting, quantifying, and explaining significant model changes. Our approach is based on an adaptation of the well-known Permutation Feature Importance (PFI) measure. It includes two hyperparameters that control the sensitivity and directly influence explanation frequency, so that a human user can adjust the method to individual requirements and application needs. We assess and validate our method’s efficacy on illustrative synthetic data streams with three popular model classes.}},
  author       = {{Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}},
  issn         = {{0933-1875}},
  journal      = {{KI - Künstliche Intelligenz}},
  keywords     = {{Artificial Intelligence}},
  number       = {{3-4}},
  pages        = {{211--224}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Agnostic Explanation of Model Change based on Feature Importance}}},
  doi          = {{10.1007/s13218-022-00766-6}},
  volume       = {{36}},
  year         = {{2022}},
}

@inbook{58243,
  author       = {{Lehberger, Regine}},
  booktitle    = {{Forschen + Lernen. Wissenschaftliches Denken als Voraussetzung für problemlösungsorientiertes Handeln. }},
  publisher    = {{UniPrint}},
  title        = {{{Digitale Medien in Schule und Unterricht}}},
  doi          = {{10.25819/UBSI/10158}},
  year         = {{2022}},
}

@misc{37082,
  author       = {{Peckhaus, Volker}},
  booktitle    = {{Mathematical Reviews, MR4090716}},
  title        = {{{Serfati, Michael, Leibniz and the Invention of Mathematical Transcendence, Franz Steiner Verlag: Stuttgart 2018 (Studia Leibnitiana Sonderheft; 54). }}},
  year         = {{2022}},
}

@misc{58065,
  author       = {{Woppowa, Jan and  Schweitzer, F}},
  booktitle    = {{Theologische Revue}},
  number       = {{118}},
  title        = {{{Religion noch besser unterrichten. Qualität und Qualitätsentwicklung im RU}}},
  year         = {{2022}},
}

@book{58282,
  editor       = {{Woppowa, Jan and Verburg, W}},
  title        = {{{Judentum und Christentum im Dialog}}},
  volume       = {{1}},
  year         = {{2022}},
}

@book{58288,
  author       = {{Woppowa, Jan}},
  title        = {{{Konfessionsbezogene Differenzsensibilität im Religionsunterricht für alle. Analysen - Interpretationen - Empfehlungen}}},
  year         = {{2022}},
}

@inproceedings{58301,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>The comprehensive implementation of digital technologies in product manufacturing leads to changes in engineering processes and requires new approaches to data management. An important role belongs to the processes of organizing the collection, storage and reuse of research data obtained and used in the process of product, system or technology development, taking into account the FAIR data principles. This article describes a Research Data Management System for the organization of documentation and measurement requests in the research and development of new oxygen-free production technologies.</jats:p>}},
  author       = {{Mozgova, Iryna and Altun, Osman and Sheveleva, T. and Castro, A. and Oladazimi, P. and Koepler, O. and Lachmayer, R. and Auer, S.}},
  booktitle    = {{Proceedings of the Design Society}},
  issn         = {{2732-527X}},
  pages        = {{525--532}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Knowledge Annotation within Research Data Management System for Oxygen-Free Production Technologies}}},
  doi          = {{10.1017/pds.2022.54}},
  volume       = {{2}},
  year         = {{2022}},
}

@inproceedings{58297,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Manually exploring the solution space for different variants of a product for a given set of requirements is ineffective regarding product development time and adaptation to dynamic customer requirements. Variant generation coupled to optimization algorithms offers possibilities to search the solution space in an automated way. This paper provides a framework to build a generative parametric design environment for functional assemblies by implementing analysis as well as synthesis methods in computer-aided tools. The procedure is presented using the example of a coffee machine.</jats:p>}},
  author       = {{Altun, Osman and Yinanc, Kutay and Mozgova, Iryna and Lachmayer, Roland}},
  booktitle    = {{Proceedings of the Design Society}},
  issn         = {{2732-527X}},
  pages        = {{553--562}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Procedure to Create an Automated Design Environment for Functional Assemblies}}},
  doi          = {{10.1017/pds.2022.57}},
  volume       = {{2}},
  year         = {{2022}},
}

@inbook{58300,
  author       = {{Siqueira, Renan and Altun, Osman and Gembarski, Paul and Lachmayer, Roland}},
  booktitle    = {{Springer Proceedings in Mathematics &amp; Statistics}},
  isbn         = {{9783030773052}},
  issn         = {{2194-1009}},
  publisher    = {{Springer International Publishing}},
  title        = {{{A Hydraulic Delta-Robot-Based Test Bench for Validation of Smart Products}}},
  doi          = {{10.1007/978-3-030-77306-9_6}},
  year         = {{2022}},
}

@inbook{58299,
  author       = {{Dierend, Hauke and Altun, Osman and Mozgova, Iryna and Lachmayer, Roland}},
  booktitle    = {{Lecture Notes in Networks and Systems}},
  isbn         = {{9783031162800}},
  issn         = {{2367-3370}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Management of Research Field Data Within the Concept of Digital Twin}}},
  doi          = {{10.1007/978-3-031-16281-7_20}},
  year         = {{2022}},
}

@inbook{58332,
  author       = {{Schulte Eickholt, Swen}},
  booktitle    = {{Transkulturelle Wechselwirkungen durch Künste und Soziales: Iranische Diaspora in Europa und darüber hinaus}},
  editor       = {{Nowrousian, Shirin}},
  isbn         = {{978-3-8260-7652-7}},
  keywords     = {{Navid Kermani, Theater, Performance, Autofiktion, Dein Name}},
  pages        = {{171--182}},
  publisher    = {{Königshausen und Neumann}},
  title        = {{{Die Ta'ziyeh als poetisches Prinzip von Navid Kermanis Roman Dein Name}}},
  year         = {{2022}},
}