@article{24952, author = {{Bauer, Anna and Sacher, Marc and Brassat, Katharina}}, journal = {{hochschullehre}}, title = {{{Studentische Akzeptanz und Relevanzwahrnehmung eines disziplinspezifischen Workshops „Wissenschaftliche Vorträge in der Physik“}}}, volume = {{6}}, year = {{2020}}, } @article{24956, author = {{Bauer, Anna and Reinhold, Peter and Sacher, Marc}}, journal = {{Phydid B, Didaktik der Physik, Beiträge zur DPG-Frühjahrstagung}}, pages = {{389--396}}, title = {{{Entwicklung eines Bewertungsmodells zur handlungsorientierten Messung experimenteller Kompetenz (Physik)Studierender}}}, year = {{2020}}, } @inbook{24957, author = {{Bauer, Anna and Reinhold, Peter and Sacher, Marc}}, booktitle = {{Naturwissenschaftliche Kompetenzen in der Gesellschaft von morgen }}, editor = {{Habig, Sebastian}}, pages = {{106--114}}, publisher = {{Gesellschaft für Didaktik der Chemie und Physik}}, title = {{{Bewertungsmodell zur experimentellen Performanz (Physik)Studierender}}}, year = {{2020}}, } @article{24973, abstract = {{Die Frage, wie sich die Weiterentwicklung der Lehre an Hochschulen systematisch verankern lässt, erfährt mit dem Auslaufen von Förderprogrammen wie dem QPL erneute Aufmerksamkeit. Bislang fehlt es an einer kontextspezifischen Theorie, die lehrbezogenen Wandel an Hochschulen analysier- und gestaltbar macht. In jedem Fall sind Change-Konzepte aus dem betriebswirtschaftlichen Bereich nur sehr beschränkt auf Hochschulen übertragbar. Demgegenüber gibt neuere Forschung Hinweise darauf, welche Kernkategorien eine hochschulspezifische Change- Theorie umfassen könnte. Darauf aufbauend schlägt der Beitrag zwei Konzepte als Kernkategorien einer Theorie lehrbezogenen Wandels an Hochschulen vor. }}, author = {{Jenert, Tobias}}, journal = {{Zeitschrift für Hochschulentwicklung}}, keywords = {{educational development, change management, educational innovation}}, number = {{4}}, pages = {{204--222.}}, title = {{{Überlegungen auf dem Weg zu einer Theorie lehrbezogenen Wandels an Hochschulen}}}, doi = {{10.3217/zfhe-15-04/12 }}, volume = {{15}}, year = {{2020}}, } @unpublished{19603, abstract = {{Micro- and smart grids (MSG) play an important role both for integrating renewable energy sources in conventional electricity grids and for providing power supply in remote areas. Modern MSGs are largely driven by power electronic converters due to their high efficiency and flexibility. Nevertheless, controlling MSGs is a challenging task due to highest requirements on energy availability, safety and voltage quality within a wide range of different MSG topologies. This results in a high demand for comprehensive testing of new control concepts during their development phase and comparisons with the state of the art in order to ensure their feasibility. This applies in particular to data-driven control approaches from the field of reinforcement learning (RL), whose stability and operating behavior can hardly be evaluated a priori. Therefore, the OpenModelica Microgrid Gym (OMG) package, an open-source software toolbox for the simulation and control optimization of MSGs, is proposed. It is capable of modeling and simulating arbitrary MSG topologies and offers a Python-based interface for plug \& play controller testing. In particular, the standardized OpenAI Gym interface allows for easy RL-based controller integration. Besides the presentation of the OMG toolbox, application examples are highlighted including safe Bayesian optimization for low-level controller tuning.}}, author = {{Bode, Henrik and Heid, Stefan Helmut and Weber, Daniel and Hüllermeier, Eyke and Wallscheid, Oliver}}, booktitle = {{arXiv:2005.04869}}, title = {{{Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control}}}, year = {{2020}}, } @inproceedings{19606, abstract = {{Mobile shopping apps have been using Augmented Reality (AR) in the last years to place their products in the environment of the customer. While this is possible with atomic 3D objects, there is is still a lack in the runtime configuration of 3D object compositions based on user needs and environmental constraints. For this, we previously developed an approach for model-based AR-assisted product configuration based on the concept of Dynamic Software Product Lines. In this demonstration paper, we present the corresponding tool support ProConAR in the form of a Product Modeler and a Product Configurator. While the Product Modeler is an Angular web app that splits products (e.g. table) up into atomic parts (e.g. tabletop, table legs, funnier) and saves it within a configuration model, the Product Configurator is an Android client that uses the configuration model to place different product configurations within the environment of the customer. We show technical details of our ready to use tool-chain ProConAR by describing its implementation and usage as well as pointing out future research directions.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Schmidt, Eugen and Engels, Gregor}}, booktitle = {{Human-Centered Software Engineering. HCSE 2020}}, editor = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}}, keywords = {{Product Configuration, Augmented Reality, Model-based, Tool Support}}, location = {{Eindhoven}}, publisher = {{Springer}}, title = {{{ProConAR: A Tool Support for Model-based AR Product Configuration}}}, doi = {{10.1007/978-3-030-64266-2_14}}, volume = {{12481}}, year = {{2020}}, } @inproceedings{19607, abstract = {{Modern services consist of modular, interconnected components, e.g., microservices forming a service mesh. To dynamically adjust to ever-changing service demands, service components have to be instantiated on nodes across the network. Incoming flows requesting a service then need to be routed through the deployed instances while considering node and link capacities. Ultimately, the goal is to maximize the successfully served flows and Quality of Service (QoS) through online service coordination. Current approaches for service coordination are usually centralized, assuming up-to-date global knowledge and making global decisions for all nodes in the network. Such global knowledge and centralized decisions are not realistic in practical large-scale networks. To solve this problem, we propose two algorithms for fully distributed service coordination. The proposed algorithms can be executed individually at each node in parallel and require only very limited global knowledge. We compare and evaluate both algorithms with a state-of-the-art centralized approach in extensive simulations on a large-scale, real-world network topology. Our results indicate that the two algorithms can compete with centralized approaches in terms of solution quality but require less global knowledge and are magnitudes faster (more than 100x).}}, author = {{Schneider, Stefan Balthasar and Klenner, Lars Dietrich and Karl, Holger}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{distributed management, service coordination, network coordination, nfv, softwarization, orchestration}}, publisher = {{IEEE}}, title = {{{Every Node for Itself: Fully Distributed Service Coordination}}}, year = {{2020}}, } @inproceedings{19609, abstract = {{Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks.}}, author = {{Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{self-driving networks, self-learning, network coordination, service coordination, reinforcement learning, deep learning, nfv}}, publisher = {{IEEE}}, title = {{{Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}}}, year = {{2020}}, } @article{19939, author = {{Kreusser, Lisa Maria and McLachlan, Robert I and Offen, Christian}}, issn = {{0951-7715}}, journal = {{Nonlinearity}}, number = {{5}}, pages = {{2335--2363}}, title = {{{Detection of high codimensional bifurcations in variational PDEs}}}, doi = {{10.1088/1361-6544/ab7293}}, volume = {{33}}, year = {{2020}}, } @phdthesis{19947, abstract = {{Ordinary differential equations (ODEs) and partial differential equations (PDEs) arise in most scientific disciplines that make use of mathematical techniques. As exact solutions are in general not computable, numerical methods are used to obtain approximate solutions. In order to draw valid conclusions from numerical computations, it is crucial to understand which qualitative aspects numerical solutions have in common with the exact solution. Symplecticity is a subtle notion that is related to a rich family of geometric properties of Hamiltonian systems. While the effects of preserving symplecticity under discretisation on long-term behaviour of motions is classically well known, in this thesis (a) the role of symplecticity for the bifurcation behaviour of solutions to Hamiltonian boundary value problems is explained. In parameter dependent systems at a bifurcation point the solution set to a boundary value problem changes qualitatively. Bifurcation problems are systematically translated into the framework of classical catastrophe theory. It is proved that existing classification results in catastrophe theory apply to persistent bifurcations of Hamiltonian boundary value problems. Further results for symmetric settings are derived. (b) It is proved that to preserve generic bifurcations under discretisation it is necessary and sufficient to preserve the symplectic structure of the problem. (c) The catastrophe theory framework for Hamiltonian ODEs is extended to PDEs with variational structure. Recognition equations for A-series singularities for functionals on Banach spaces are derived and used in a numerical example to locate high-codimensional bifurcations. (d) The potential of symplectic integration for infinite-dimensional Lie-Poisson systems (Burgers’ equation, KdV, fluid equations, . . . ) using Clebsch variables is analysed. It is shown that the advantages of symplectic integration can outweigh the disadvantages of integrating over a larger phase space introduced by a Clebsch representation. (e) Finally, the preservation of variational structure of symmetric solutions in multisymplectic PDEs by multisymplectic integrators on the example of (phase-rotating) travelling waves in the nonlinear wave equation is discussed.}}, author = {{Offen, Christian}}, publisher = {{Massey University}}, title = {{{Analysis of Hamiltonian boundary value problems and symplectic integration}}}, year = {{2020}}, }