Universität Stuttgart
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Item Open Access Development of an Euler-Lagrangian framework for point-particle tracking to enable efficient multiscale simulations of complex flows(2023) Kschidock, HelenaIn this work, we implement, test, and validate an Euler-Lagrangian point-particle tracking framework for the commercial aerodynamics and aeroacoustics simulation tool ultraFluidX, which is based on the Lattice Boltzmann Method and optimized for GPUs. Our framework successfully simulates one-way and two-way coupled particle-laden flows based on drag forces and gravitation. Trilinear interpolation is used for determining the fluid's macroscopic properties at the particle position. Object and domain boundary conditions are implemented using a planar surface approximation. The whole particle framework is run within three dedicated GPU kernels, and data is only copied back to the CPU upon output. We show validation for the velocity interpolation, gravitational acceleration, back-coupling forces and boundary conditions, and test runtimes and memory requirements. We also propose the next steps required to make the particle framework ready for use in engineering applications.Item Open Access Improving usability of gaze and voice based text entry systems(2023) Sengupta, Korok; Staab, Steffen (Prof. Dr.)Item Open Access Hochperformante Auflösung kleiner Referenzen in verteilten Systemen(2023) Waimer, JoelBereits mit dem Aufkommen erster Filesharing-Systeme wurde die Entwicklung effektiver Verfahren zum Auffinden von mittels global eindeutiger Referenzen bezeichneter Datenobjekte in Peer-to-Peer-Systemen intensiv diskutiert und vorangetrieben, mit dem Ergebnis zahlreicher konkret ausgearbeiteter Lösungsansätze. Unbeachtet geblieben ist dabei jedoch der, für Filesharing-Systeme nicht lohnenswerte, für kleinere verteilte Datenspeichersysteme aber durchaus vorteilhafte Einsatz kleiner Referenzen, in der, aufgrund neuer Fortschritte im Bereich der Speicherdichte, damit einhergehenden dünnen Besetzung dieser kleinen Adressräume, durch welche allerdings die den Verfahren zueigenen Garantien bezüglich der benötigten Anzahl an Schritten zur Auflösung einer Referenz innerhalb des Systems stark verzerrt werden und sich die je Auflösung nötige Laufzeit vergrößert. Diese Arbeit beleuchtet zunächst die Grundlagen der mit der Auflösung von Datenreferenzen in verteilten Speichersystemen einhergehenden Problematiken, beschreibt die beiden Verfahren Chord [SMK+01] und Koorde [KK03] und misst anschließend deren Leistungsfähigkeit in dünn besetzten Adressräumen, unter der Verwendung kleiner Referenzen; mit den Messungen kann schließlich die Vermutung eines negativen Einflusses der dünnen Besetztheit des Adressraums auf die benötigte Laufzeit je Auflösung bestätigt werden. Eingegangen wurde hierbei auch auf mögliche Gegenmaßnahmen zur Verbesserung der Leistungsfähigkeit der beiden Verfahren, wobei hier die Verbindung der beiden untersuchten Verfahren mit einer Abwandlung des beim Distance-Halving-Netzwerk [NW03] eingesetzten Initialisierungsverfahrens zu einer nahezu gleichmäßigen Aufteilung des Adressraumes auf die einzelnen Knoten hier großes Potential besitzt, da so einer Entartung der Pfadlänge je Auflösung entgegengewirkt werden kann; zudem zeigten sich in den Messungen stark ungünstige Auswirkungen einer naiven, iterativen Implementierung des Chord-Verfahrens gegenüber Koorde.Item Open Access A framework for distributed training of physics-informed neural networks using JAX(2021) Braun, JohannesThe intention of this thesis is to evaluate the high-performance machine learning framework JAX. In the course of this work, a physics-informed neural network that solves the Burgers’ equation is implemented. This problem is chosen, as it is a well known and researched numerical problem and thus allows for great comparability. Here, a basic version of the physics-informed neural network with Flax is first created, which is an ecosystem for JAX that allows to implement neural networks. This version was then first improved with the tools offered via JAX. Afterwards, a SPMD version of this physics-informed neural network is also implemented, where multiple graphics processor units are utilized in the training. Additionally, the physics-informed neural network is extended to predict the parameters of the partial differential equation that describes the Burgers’ equation. This was done by the physics-informed neural network, while still learning to estimate the Burgers’ equation. For the optimized basic physics-informed neural network and the physics-informed neural network that also estimates the parameters of the partial differential equation promising results with JAX were achieved. The outcome of the SPMD physics-informed neural network was dissatisfactory, as it did not yield any improvements compared to the basic version. Although, this might stem from the small amount of data points used for each iteration and further points discussed in this paper. Additionally, a caveat must be voiced, as it often becomes apparent that the documentations of JAX and Flax are a work in progress. Because of this, a lot of crucial features have to be found out by trial and error, while working with these frameworks. Yet still, JAXand hence also Flax are considered a compelling framework to implement high-performance neural networks. Especially because of its potent Autograd and straightforward XLA just in time compilation. Through these components a performant physics-informed neural network can be quickly setup as shown in this thesis. Here, Autograd aids in creating the necessary gradients for the physical loss. Whereas the XLA just in time compilation yields drastic improvements to the run time of the training performed on the physics-informed neural network. These features then lead to previously mentioned promising results for the basic physics-informed neural network and the physics-informed neural network that also estimates the parameters of the partial differential equation.Item Open Access Benchmarking pre-trained language models for schema-agnostic entity resolution(2024) Bothmann, JanData integration is a process in which data from different sources are brought together to create a unified picture of the data. A vital aspect of this integration is Entity Resolution, which tries to identify elements that correspond to the same entity across multiple datasets. The complexity of ER tasks can vary significantly, as data exhibits different characteristics and levels of structuredness, which can influence the difficulty of the task. In this thesis, we evaluate how current state-of-the-art Entity Resolution systems perform when dealing with semi-structured data. To do this, several semi-structured ER benchmarks covering data from various domains were created for evaluation. Additionally, to explore how different data characteristics or other influencing factors impact the performance of matching systems, we developed the Benchmark Creator. This tool allows us and other users to generate benchmarks where data exhibits specific characteristics that may influence the complexity of the ER task. We used Ditto, Sudowoodo and the GPT4o-mini model to evaluate performance on the newly created benchmarks. Our evaluation reveals that Ditto and the GPT4o-mini model can effectively perform schema-agnostic ER on semi-structured data.Item Open Access Optimization of diffusive load-balancing for short-range molecular dynamics(2020) Hauser, SimonIn recent years, multi-core processors have become more and more important for manufacturers, which means that developers now have to think more about how to distribute a single application sensibly over several processes. This is where load balancing comes in, allowing us to move load from an overloaded process to an underloaded process. One way of load balancing is diffusive load balancing, which is a method of moving load in the local neighborhood and therefore no global communication is needed. The advantage of this is that processes that have completed the local communication and thus the load-balancing process can continue with the next calculations. This form of load balancing is found in librepa, a library that deals with the balancing of linked-cell grids and can be used in the simulation software ESPResSo. In the course of this thesis the library has been extended with the First and Second Order Diffusion. Furthermore, a feature was added that allows to keep the initial structure of the grid constant, which means that the neighborhood of each process does not change. This feature is necessary for the Second Order Diffusion. A comparison between the methods shows that both First and Second Order Diffusion distribute the load better in the system than librepa's default and prior to this work only diffusive variant. Furthermore, we show that there is no significant overhead in using the Preserving Structure Diffusion. With the use of flow iteration the imbalance values of First and Second Order Diffusion can be improved even further.Item Open Access Models for data-efficient reinforcement learning on real-world applications(2021) Dörr, Andreas; Toussaint, Marc (Prof. Dr.)Large-scale deep Reinforcement Learning is strongly contributing to many recently published success stories of Artificial Intelligence. These techniques enabled computer systems to autonomously learn and master challenging problems, such as playing the game of Go or complex strategy games such as Star-Craft on human levels or above. Naturally, the question arises which problems could be addressed with these Reinforcement Learning technologies in industrial applications. So far, machine learning technologies based on (semi-)supervised learning create the most visible impact in industrial applications. For example, image, video or text understanding are primarily dominated by models trained and derived autonomously from large-scale data sets with modern (deep) machine learning methods. Reinforcement Learning, on the opposite side, however, deals with temporal decision-making problems and is much less commonly found in the industrial context. In these problems, current decisions and actions inevitably influence the outcome and success of a process much further down the road. This work strives to address some of the core problems, which prevent the effective use of Reinforcement Learning in industrial settings. Autonomous learning of new skills is always guided by existing priors that allow for generalization from previous experience. In some scenarios, non-existing or uninformative prior knowledge can be mitigated by vast amounts of experience for a particular task at hand. Typical industrial processes are, however, operated in very restricted, tightly calibrated operating points. Exploring the space of possible actions or changes to the process naively on the search for improved performance tends to be costly or even prohibitively dangerous. Therefore, one reoccurring subject throughout this work is the emergence of priors and model structures that allow for efficient use of all available experience data. A promising direction is Model-Based Reinforcement Learning, which is explored in the first part of this work. This part derives an automatic tuning method for one of themostcommonindustrial control architectures, the PID controller. By leveraging all available data about the system’s behavior in learning a system dynamics model, the derived method can efficiently tune these controllers from scratch. Although we can easily incorporate all data into dynamics models, real systems expose additional problems to the dynamics modeling and learning task. Characteristics such as non-Gaussian noise, latent states, feedback control or non-i.i.d. data regularly prevent using off-the-shelf modeling tools. Therefore, the second part of this work is concerned with the derivation of modeling solutions that are particularly suited for the reinforcement learning problem. Despite the predominant focus on model-based reinforcement learning as a promising, data-efficient learning tool, this work’s final part revisits model assumptions in a separate branch of reinforcement learning algorithms. Again, generalization and, therefore, efficient learning in model-based methods is primarily driven by the incorporated model assumptions (e.g., smooth dynamics), which real, discontinuous processes might heavily violate. To this end, a model-free reinforcement learning is presented that carefully reintroduces prior model structure to facilitate efficient learning without the need for strong dynamic model priors. The methods and solutions proposed in this work are grounded in the challenges experienced when operating with real-world hardware systems. With applications on a humanoid upper-body robot or an autonomous model race car, the proposed methods are demonstrated to successfully model and master their complex behavior.Item Open Access Concepts and methods for the design, configuration and selection of machine learning solutions in manufacturing(2021) Villanueva Zacarias, Alejandro Gabriel; Mitschang, Bernhard (Prof. Dr.-Ing. habil.)The application of Machine Learning (ML) techniques and methods is common practice in manufacturing companies. They assign teams to the development of ML solutions to support individual use cases. This dissertation refers as ML solution to the set of software components and learning algorithms to deliver a predictive capability based on available use case data, their (hyper) paremeters and technical settings. Currently, development teams face four challenges that complicate the development of ML solutions. First, they lack a formal approach to specify ML solutions that can trace the impact of individual solution components on domain-specific requirements. Second, they lack an approach to document the configurations chosen to build an ML solution, therefore ensuring the reproducibility of the performance obtained. Third, they lack an approach to recommend and select ML solutions that is intuitive for non ML experts. Fourth, they lack a comprehensive sequence of steps that ensures both best practices and the consideration of technical and domain-specific aspects during the development process. Overall, the inability to address these challenges leads to longer development times and higher development costs, as well as less suitable ML solutions that are more difficult to understand and to reuse. This dissertation presents concepts to address these challenges. They are Axiomatic Design for Machine Learning (AD4ML), the ML solution profiling framework and AssistML. AD4ML is a concept for the structured and agile specification of ML solutions. AD4ML establishes clear relationships between domain-specific requirements and concrete software components. AD4ML specifications can thus be validated regarding domain expert requirements before implementation. The ML solution profiling framework employs metadata to document important characteristics of data, technical configurations, and parameter values of software components as well as multiple performance metrics. These metadata constitute the foundations for the reproducibility of ML solutions. AssistML recommends ML solutions for new use cases. AssistML searches among documented ML solutions those that better fulfill the performance preferences of the new use case. The selected solutions are then presented to decision-makers in an intuitive way. Each of these concepts was evaluated and implemented. Combined, these concepts offer development teams a technology-agnostic approach to build ML solutions. The use of these concepts brings multiple benefits, i. e., shorter development times, more efficient development projects, and betterinformed decisions about the development and selection of ML solutions.Item Open Access Anonymisierung von Daten : von der Literatur zum Automobilbereich(2023) Herkommer, JanDie Datenanonymisierung im Automobilbereich gewinnt immer mehr an Bedeutung. Jedoch gibt es kaum Literatur und Ansätze, die sich mit der Anonymisierung von Automobildaten beschäftigen. In dieser Arbeit werden deshalb mit Hilfe einer strukturierten Literaturrecherche die aktuell verbreitetsten Verfahren und Anwendungsbereiche erörtert und die wichtigsten Erkenntnisse der Recherche zusammengefasst. So werden bei den analysierten Paper der Anwendungsbereich, die Methodik sowie der zu anonymisierende Datentyp ermittelt. DesWeiteren werden die Metriken zum Vergleich von unterschiedlichen Ansätzen betrachtet. Mit Hilfe dieser Erkenntnisse wird im Anschluss auf die Anonymisierung von Fahrzeugdaten anhand verschiedener Anwendungsfälle eingegangen und Herausforderungen und Lösungsansätze skizziert. Zuletzt wird beispielhaft ein Ansatz zur Anonymisierung von Routen implementiert, um mit Hilfe eines GPS-Sensors aufgezeichnete Fahrzeugrouten zu anonymisieren. Dabei werden zusätzliche Probleme wie der Umgang mit Messungenauigkeiten und Messfehlern sowie die tatsächlichen Auswirkungen von reduzierter Datennutzbarkeit verdeutlicht.Item Open Access Webanwendung für Multiphysik-Simulationen mit opendihu(2020) Tompert, MatthiasOpendihu ist ein Software-Framework zum Lösen von Multi-Physik-Problemen mit Hilfe der Finiten-Elemente-Methode. Die Anwendungen von Opendihu sind hauptsächlich im Bereich der Skelett-Muskel-Simulationen. Das Erstellen einer Simulation in Opendihu erfolgt über eine C++-Datei, in welcher verschachtelte Löserstrukturen angegeben werden und über eine Python-Datei in welcher die Parameter der verwendeten Löser konfiguriert werden. Das Bearbeiten vorhandener Simulationen und das Erstellen neuer Simulationen mit Hilfe dieser Schnittstelle erfordern gute Kenntnisse über den Sourcecode, beziehungsweise die Struktur von Opendihu. Daher wäre es Sinnvoll Opendihu um eine Nutzerfreundlichere und auch für Einsteiger geeignete Nutzerschnittstelle zu erweitern. Im Rahmen dieser Arbeit habe Ich daher eine grafische Benutzeroberfläche für Opendihu implementiert, welche die Löserstruktur und die Parameter der einzelnen Löser einer Simulation visualisiert. Außerdem ist es mit der Anwendung möglich vorhandene Simulationen zu ändern und neue Simulationen mit Hilfe eines Baukastensystems zu erstellen. Diese Bachelorarbeit erläutert den Aufbau dieser Anwendung und erforscht mit Hilfe einer Nutzerstudie ob die entstandene Benutzerschnittstelle einen Mehrwert gegenüber der bereits vorhandenen Schnittstelle bietet. Das Bearbeiten und Erstellen neuer Simulationen mit Hilfe der Anwendung wurde von den Teilnehmern der Studie im Durchschnitt als einfacher empfunden, als das Bearbeiten und Erstellen neuer Simulationen mit Hilfe der bereits vorhandenen Schnittstelle. Die entstandene Anwendung bietet also einen Mehrwert beim Bearbeiten und Erstellen von Opendihu-Simulationen. Besonders beim Erstellen neuer Simulationen wurde das Baukastensystem als hilfreich bewertet.