@unpublished{46579, abstract = {{The Koopman operator has become an essential tool for data-driven analysis, prediction and control of complex systems, the main reason being the enormous potential of identifying linear function space representations of nonlinear dynamics from measurements. Until now, the situation where for large-scale systems, we (i) only have access to partial observations (i.e., measurements, as is very common for experimental data) or (ii) deliberately perform coarse graining (for efficiency reasons) has not been treated to its full extent. In this paper, we address the pitfall associated with this situation, that the classical EDMD algorithm does not automatically provide a Koopman operator approximation for the underlying system if we do not carefully select the number of observables. Moreover, we show that symmetries in the system dynamics can be carried over to the Koopman operator, which allows us to massively increase the model efficiency. We also briefly draw a connection to domain decomposition techniques for partial differential equations and present numerical evidence using the Kuramoto--Sivashinsky equation.}}, author = {{Peitz, Sebastian and Harder, Hans and Nüske, Feliks and Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl}}, booktitle = {{arXiv:2307.15325}}, title = {{{Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems}}}, year = {{2023}}, } @article{23428, abstract = {{The Koopman operator has become an essential tool for data-driven approximation of dynamical (control) systems in recent years, e.g., via extended dynamic mode decomposition. Despite its popularity, convergence results and, in particular, error bounds are still quite scarce. In this paper, we derive probabilistic bounds for the approximation error and the prediction error depending on the number of training data points; for both ordinary and stochastic differential equations. Moreover, we extend our analysis to nonlinear control-affine systems using either ergodic trajectories or i.i.d. samples. Here, we exploit the linearity of the Koopman generator to obtain a bilinear system and, thus, circumvent the curse of dimensionality since we do not autonomize the system by augmenting the state by the control inputs. To the best of our knowledge, this is the first finite-data error analysis in the stochastic and/or control setting. Finally, we demonstrate the effectiveness of the proposed approach by comparing it with state-of-the-art techniques showing its superiority whenever state and control are coupled.}}, author = {{Nüske, Feliks and Peitz, Sebastian and Philipp, Friedrich and Schaller, Manuel and Worthmann, Karl}}, journal = {{Journal of Nonlinear Science}}, title = {{{Finite-data error bounds for Koopman-based prediction and control}}}, doi = {{10.1007/s00332-022-09862-1}}, volume = {{33}}, year = {{2023}}, } @article{21600, abstract = {{Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamics, an equidistant discretization is often inadvisable as it results in prohibitively large errors or computational effort. To this end, adaptive schemes, such as solvers based on Runge–Kutta pairs, have been developed which adapt the step size based on local error estimations at each step. While the classical schemes apply very generally and are highly efficient on regular systems, they can behave suboptimally when an inefficient step rejection mechanism is triggered by structurally complex systems such as chaotic systems. To overcome these issues, we propose a method to tailor numerical schemes to the problem class at hand. This is achieved by combining simple, classical quadrature rules or ODE solvers with data-driven time-stepping controllers. Compared with learning solution operators to ODEs directly, it generalizes better to unseen initial data as our approach employs classical numerical schemes as base methods. At the same time it can make use of identified structures of a problem class and, therefore, outperforms state-of-the-art adaptive schemes. Several examples demonstrate superior efficiency. Source code is available at https://github.com/lueckem/quadrature-ML.}}, author = {{Dellnitz, Michael and Hüllermeier, Eyke and Lücke, Marvin and Ober-Blöbaum, Sina and Offen, Christian and Peitz, Sebastian and Pfannschmidt, Karlson}}, journal = {{SIAM Journal on Scientific Computing}}, number = {{2}}, pages = {{A579--A595}}, title = {{{Efficient time stepping for numerical integration using reinforcement learning}}}, doi = {{10.1137/21M1412682}}, volume = {{45}}, year = {{2023}}, } @inproceedings{46739, author = {{Sadeghi-Kohan, Somayeh and Hellebrand, Sybille and Wunderlich, Hans-Joachim}}, booktitle = {{2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)}}, publisher = {{IEEE}}, title = {{{Low Power Streaming of Sensor Data Using Gray Code-Based Approximate Communication}}}, doi = {{10.1109/dsn-w58399.2023.00056}}, year = {{2023}}, } @inproceedings{46757, author = {{Schwerin, Imke and Häsel-Weide, Uta}}, booktitle = {{International Symposium in Elementary Mathematics Teaching. Proceedings: New Directions in Elementary Mathematics Education}}, editor = {{Novotna, J. and Moraova, H.}}, location = {{Prag}}, pages = {{297--305}}, publisher = {{Charles University}}, title = {{{Second grader´s understanding of doubling and halfing in various representations}}}, year = {{2023}}, } @inbook{46758, author = {{Schmidt, Rebekka and Tenberge, Claudia and Häsel-Weide, Uta}}, booktitle = {{Aktive Teilhabe fördern – ICM und Student Engagement in der Hochschullehre}}, editor = {{Vöing, N. and Schmidt, R. and Neiske, I.}}, pages = {{297--318}}, publisher = {{Visual Ink Publishing}}, title = {{{Lehre in Zeiten von Digitalisierung und Inklusion - Beispiele aus drei Fächern}}}, year = {{2023}}, } @book{44719, abstract = {{„Lerne deinen Körper besser kennen“, „Das Beste für deine Gesundheit“ und „Ihre Transformation beginnt jetzt“ - mit Versprechen wie diesen vermitteln die Produkttexte von Wearables wie Fitnesstracker und Smartwatches ein ganz bestimmtes Bild ihrer vorgesehenen Nutzer*innen und deren Nutzung. Verbunden mit den kleinen, am Handgelenk getragenen Geräten sind Fragen nach Erkenntnisgewinn und Kontrollverlust, Selbstoptimierung und Quantifizierungslogiken, Eigenverantwortung und Fremdsteuerung. Die vorliegende Arbeit widmet sich diesem komplexen Spannungsfeld und verfolgt dabei einen multiperspektivischen Ansatz: im Rahmen einer Dispositivanalyse werden die einzelnen Elemente des Wearable-Dispositivs als eigenständige, empirisch zu untersuchende Analysegegenstände betrachtet, um so das Zusammenwirken und die komplexe Beziehung von Diskursen, Gegenständen, Nutzung, Subjekten und Gesellschaft zu erforschen. Ein besonderes Erkenntnisinteresse liegt dabei auf dem Wissen, was sich über Wearables etabliert hat und sich in den Alltagspraktiken der Nutzer*innen widerspiegelt sowie bei der Frage nach den möglichen Funktionen und Auswirkungen des Wearable-Dispositivs. }}, author = {{Schloots, Franziska Margarete}}, isbn = {{9783658409012}}, issn = {{2512-112X}}, keywords = {{Selbstvermessung, Dispositivanalyse, Gesundheitsdiskurs, Quantifizierungsgesellschaft, Wearables, Selbstoptimierung}}, publisher = {{Springer Fachmedien Wiesbaden}}, title = {{{Mit dem Leben Schritt halten - Eine Analyse des Wearable-Dispositivs}}}, doi = {{10.1007/978-3-658-40902-9}}, year = {{2023}}, } @article{46784, author = {{Wallscheid, Oliver and Peitz, Sebastian and Stenner, Jan and Weber, Daniel and Boshoff, Septimus and Meyer, Marvin and Chidananda, Vikas and Schweins, Oliver}}, issn = {{2475-9066}}, journal = {{Journal of Open Source Software}}, keywords = {{General Earth and Planetary Sciences, General Environmental Science}}, number = {{89}}, publisher = {{The Open Journal}}, title = {{{ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids}}}, doi = {{10.21105/joss.05616}}, volume = {{8}}, year = {{2023}}, } @article{46186, author = {{Höper, Lukas and Schulte, Carsten}}, issn = {{0025-5866}}, journal = {{MNU journal}}, number = {{4}}, pages = {{314--320}}, publisher = {{Verlag Klaus Seeberger}}, title = {{{Paradigmenwechsel vom klassischen zum datengetriebenen Problemlösen im Informatikunterricht}}}, volume = {{76}}, year = {{2023}}, } @inproceedings{35014, author = {{Blömer, Johannes and Bobolz, Jan and Bröcher, Henrik}}, location = {{Taipeh, Taiwan}}, title = {{{On the impossibility of surviving (iterated) deletion of weakly dominated strategies in rational MPC}}}, year = {{2023}}, } @inproceedings{43458, author = {{Blömer, Johannes and Bobolz, Jan and Porzenheim, Laurens Alexander}}, location = {{Guangzhou, China}}, title = {{{A Generic Construction of an Anonymous Reputation System and Instantiations from Lattices}}}, year = {{2023}}, } @inproceedings{46959, author = {{Vernholz, Mats and Temmen, Katrin}}, location = {{Flensburg}}, title = {{{Gewerblich-technische Lehrkräftebildung in Deutschland – Analyse der Einflüsse auf das akademische Selbstkonzept von Lehramtsstudierenden technischer (beruflicher) Fachrichtungen}}}, year = {{2023}}, } @inproceedings{47050, author = {{Wecker, Daniel and Yigitbas, Enes}}, booktitle = {{Proceedings of the ACM Symposium on Spatial User Interaction (SUI 2023)}}, publisher = {{ACM}}, title = {{{Minimizing Eye Movements and Distractions in Head-Mounted Augmented Reality through Eye-Gaze Adaptiveness}}}, year = {{2023}}, } @article{47051, author = {{Yigitbas, Enes and Schmidt, Maximilian and Bucchiarone, Antonio and Gottschalk, Sebastian and Engels, Gregor}}, journal = {{Science of Computer Programming}}, publisher = {{Elsevier}}, title = {{{GaMoVR: Gamification-Based UML Learning Environment in Virtual Reality}}}, year = {{2023}}, } @inproceedings{47057, author = {{Schmidt, Leonard and Yigitbas, Enes}}, booktitle = {{Proceedings of the 27th International Workshop on Personalization and Recommendation}}, publisher = {{GI DL}}, title = {{{Transitional Cross Reality Interfaces for Spatially Demanding Search and Collect Tasks }}}, year = {{2023}}, } @inproceedings{47055, author = {{Neumayr, Thomas and Yigitbas, Enes and Augstein, Mirjam and Herder, Eelco}}, booktitle = {{Proceedings of the Mensch & Computer (2023)}}, title = {{{ABIS 2023 – 27th International Workshop on Personalization and Recommendation}}}, year = {{2023}}, } @misc{47134, author = {{Deppe, Volker}}, title = {{{Routing in Hypergraphs}}}, year = {{2023}}, } @inproceedings{47150, author = {{Yigitbas, Enes and Witalinski, Iwo and Gottschalk, Sebastian and Engels, Gregor}}, booktitle = {{Proceedings of the 24th International Conference on Product-Focused Software Process Improvement (PROFES 2023)}}, publisher = {{Springer}}, title = {{{Virtual Reality Collaboration Platform for Agile Software Development}}}, year = {{2023}}, } @inproceedings{46813, abstract = {{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 = {{Wohlleben, Meike Claudia and Muth, Lars and Peitz, Sebastian and Sextro, Walter}}, booktitle = {{Proceedings in Applied Mathematics and Mechanics}}, issn = {{1617-7061}}, keywords = {{Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics}}, publisher = {{Wiley}}, title = {{{Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits}}}, doi = {{10.1002/pamm.202300039}}, year = {{2023}}, } @inbook{47075, abstract = {{Physics textbooks are generally viewed as important tools that provide well-presented and reliable information that supports and enhances students' understanding of critical concepts. The main goal of this chapter is to find out what attention has been given to textbook evaluation in physics education research literature. Studies about physics textbooks from different countries and different eras are discussed and analyzed, and a broad overview about aspects that influence the efficacy of physics textbooks are presented. Research papers that discuss the importance and influence of digital textbooks (and similar technological resources) are also analyzed.}}, author = {{Kapanadze, Marika and Jonas-Ahrend, Gabriela and Mazzolini, Alexander and Joubran, Fadeel}}, booktitle = {{The International Handbook of Physics Education Research: Special Topics}}, isbn = {{9780735425484}}, publisher = {{AIP Publishing LLCMelville, New York}}, title = {{{Evaluation of Physics Textbooks}}}, doi = {{10.1063/9780735425514_017}}, year = {{2023}}, }