Florian Schneider: Transformer Models for Cross-Modal Text-Image Retrieval.
When the Transformer architecture was first introduced, its target research domain was Natural Language Processing, where the famous BERT model pushed the boundaries in several downstream tasks. Motivated by these breakthroughs, other research fields like Computer Vision started to leverage Transformers with great success.
In state-of-the-art cross-modal text-image retrieval, where images are searched via textual queries, the acquired knowledge from both fields is combined to significantly improve the quality of retrieved images. Further, employing Transformer models enables computing not only global text-image similarity but also fine-granular word-region alignments, which allows in-image search, useful for many real world applications.

Lina Fesefeldt: Second Order Information in Neural Network Training.
When choosing an optimizer for your neural network, you will probably consider methods that are based only on first order derivatives, like Stochastic Gradient Descent or Adam. But recent research suggests that second order optimizers are also applicable for neural network training. This is possible via implicit Hessian-vector products.
This talk tackles the problem of minimizing your cost function from a mathematical point of view. While talking about the use of second order optimizers in neural network training, we will stumble across some interesting properties of neural networks and their cost functions.

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Daniel Stolpmann: Distributed Reinforcement Learning on a High Performance Cluster Using Ray.
Ray is a Python framework for developing distributed applications. While not limited to it, it has a strong focus on Machine Learning and supports it with a variety of included libraries. For example, Ray RLlib provides implementations of many common Reinforcement Learning algorithms, which reduces development time and allows the user to quickly compare different approaches to best solve the problem at hand. As Reinforcement Learning algorithms can be very sensitive to the choice of hyperparameters, Ray Tune can be used to tune them via optimization-based methods. While this typically requires a large number of simulation runs, Ray can speed up the process by running them in parallel on multiple processors or machines.
This talk gives an introduction to Ray, its libraries and how they can be used to seamlessly scale Reinforcement Learning applications from a laptop to the High Performance Cluster (HPC) of the TUHH.

Cornelia Hofsäß: Pulmonary Embolus Detection with Dual-Energy CT Data Augmentation.
3D segmentation U-Nets are trained for pulmonary embolus (PE) detection on three different data sets. We investigate the impact of the training data set on the generalization capabilities and use dual-energy CT data augmentation to increase performance.

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Mijail Guillemard: Tangential bundles, curvature and persistent homology for material defect detection.
An application to the detection of material defects using persistent homology is presented. We combine tangent bundles and curvature with persistent homology in the context of machine learning.

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Trishita Banerjee: Machine Learning in Security.
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Denys Romanenko: Holistic process monitoring with machine learning classification methods using internal machine sensors for semi-automatic drilling.
Since one third of rivet holes during aircraft assembly are produced with semi-automatic drilling units, in this work reliable and efficient methods for process state prediction using Machine Learning (ML) classification methods were developed for this application. Process states were holistically varied in the experiments, gathering motor current and machine vibration data. These data were used as input to identify the optimal combination of five data feature preparation and nine ML methods for process state prediction. K-nearest-neighbour, decision tree and artificial neural network models provided reliable predictions of the process states: workpiece material, rotational speed, feed, peck-feed amplitude and lubrication state. Data preprocessing through sequential feature selection and principal components analysis proved to be favourably for these applications. The prediction of the workpiece clamping distance revealed frequent misclassifications and thus, was not reliable.

Christian Becker & Davood Babazadeh: Einsatz von KI für die Betriebsführung elektrischer Netze.
Durch die Energiewende entwickelt sich die Betriebsführung von elektrischen Netzen zu einer immer komplexeren Aufgabe, woraus sich neue Anforderungen an die Systeme zur Netzüberwachung und Netzregelung ergeben. Im Rahmen dieses Workshops werden verschiedene Anwendungsfälle aus dem Bereich der Betriebsführung von elektrischen Netzen betrachtet, für die der Einsatz von KI möglich ist. Hierbei werden sowohl die einzelnen Anwendungen in den Grundzügen erläutert, als auch die erwarteten Vorteile durch den Einsatz der verschiedenen KI-Verfahren. Zu den behandelten Themen gehören unter anderem die Modellierung und Vorhersage von Lasten im Netz, die Fehlerdetektion und Diagnose, die Zustandsschätzung sowie die Stabilitätserfassung und -beeinflussung.

Robert Meißner & Benjamin Boll: Klassifikation handgeschriebener Zahlen mit Hilfe eines mehrschichtigen neuronalen Perceptron (MLP)-Netzes und Jupyter-Notebooks.
Ziel dieses Workshops ist ein Einblick in die Klassifikation handgeschriebener Zahlen mit Hilfe des Maschinellen Lernens zu gewähren. Nach einer kleinen Einführung in die Grundlagen über einfache neuronale Netze für diese Anwendung, Jupyter-Notebooks als Arbeitsumgebung für Python, und Keras für die Entwicklung der ANNs, wird der Workflow für eine Zeichenerkennung bearbeitet. Schlussendlich werden die trainierten Netze für die Detektion und Erkennung von handgeschriebenen Texten auf Bildern angewendet. Für die Teilnahme an diesem Workshop sind Grundkenntnisse in Python von Vorteil und eine lokale Python-Installation von Anaconda3 wird vorausgesetzt. Eine Anleitung zur Installation findet sich hier.

Benedikt Kriegesmann: Effiziente Quantifizierung von Unsicherheiten (Monte-Carlo-Simulationen) mittels Ersatzmodellen.
In vielen Ingenieuranwendungen liegen mathematische Modelle und Simulationstools vor, um für vorgegebene Eingangsgrößen relevante Zielgrößen zu ermitteln. Beispielsweise in der Strukturmechanik sind die vorgegebenen Eingangsgrößen die Geometrie und Material­parameter einer Struktur, sowie die einwirkenden Lasten. Typische Zielgrößen sind die Verformung der Struktur und die maximal auftretenden Spannungen, aus denen sich das Bauteilversagen ergibt. Tatsächlich sind die Eingangsgrößen i.d.R. nicht genau bekannt, sondern unterliegen einer stochastischen Streuung. Dementsprechend unterliegen auch die Zielgrößen einer Streuung, die sich mit probabilistischen Methoden (a.k.a. Uncertainty Quantification) bestimmen lässt. Ein weit verbreitetes, probabilistisches Verfahren ist die Monte-Carlo-Methode. Dieses sehr robuste Verfahren ist einfach zu implementieren, jedoch auch sehr rechenintensiv. Die Grundidee ist, dass die Werte der Eingangsgrößen entsprechend ihrer stochastischen Verteilung erzeugt und in das Simulationsmodell eingesetzt werden. Dies geschieht so oft, bis die Verteilung der Zielfunktion ausreichend genau bestimmt ist (z.B. bis Mittelwert und Standard­abweichung der Zielfunktion konvergieren). Das beutet, dass das Simulationsmodell sehr oft (Größenordnung 103 bis 106) ausgewertet werden muss, was bei komplexen, sehr rechenintensiven Simulationsmodellen zum Problem wird. Hier kommen Methoden des Maschinellen Lernens zum Einsatz um effiziente Ersatzmodelle (a.k.a. Surrogate Model, Meta Model) zu erzeugen. Diese Ersatzmodelle (z.B. neuronale Netze) werden zunächst trainiert und dann anstelle des eigentlichen Simulationsmodells deutlich schneller ausgewertet. Im Workshop, zeigen wir Anhand strukturmechanischer Beispiele, wie die Streuung einer Zielgröße mittels Monte-Carlo-Simulation unter Verwendung von Ersatzmodellen bestimmt werden kann.

Christian Schuster & Morten Schierholz: Einsatz und Chancen von Methoden des Maschinellen Lernens in der Elektromagnetischen Verträglichkeit.
Die Elektromagnetische Verträglichkeit (EMV) befasst sich mit der Unterdrückung ungewollter elektromagnetischer Störungen zwischen elektronischen Geräten, Systemen und Komponenten. Steigende Anforderungen im Bereich der EMV – man denke z.B. an die fortschreitende drahtlose Kommunikation bei immer höheren Frequenzen – erfordern eine kontinuierliche Entwicklung der ingenieurwissenschaftlichen Methoden, um früh und kostengünstig die richtigen Entscheidungen bei der Entwicklung zu treffen. In diesem Workshop werden verschiedene Methoden des Maschinellen Lernens vorgestellt, die in den EMV-Anwendungsfeldern der Signalintegrität (signal integrity) von drahtgebundenen Kanälen sowie der Kontrolle der Spannungsversorgung (power integrity) und der Abstrahlung (electromagnetic interference) von elektronischen Komponenten und Systemen aktuell erforscht werden. Eigene Forschungen im Bereich von künstlichen Neuronalen Netzen, die zur Analyse von Leiterplattenstrukturen verwendet werden, zeigen hierbei auf, welche Chance sich für die EMV und ganz allgemein die Hardware-Entwicklung in Zukunft ergeben.

Matthias Schulte: Einführung in Support Vector Machines.
Support Vector Machines (SVM) sind ein leistungsstarkes und vielseitiges Verfahren des Maschinellen Lernens, das für viele Anwendungen eingesetzt wird. Die Grundidee von SVM ist, die zu zwei unterschiedlichen Gruppen gehörenden Daten mit einer Hyperebene zu trennen. Mit Hilfe von Transformationen in höherdimensionale Räume und der Verwendung von Kernel-Funktionen lässt sich dies auch für den Fall durchführen, dass die zugrundeliegenden Daten nicht linear getrennt werden können. Der Workshop zielt vor allem darauf ab SVM einzuführen und setzt den Schwerpunkt auf die theoretischen Grundlagen.

Mijail Guillemard: Grundlagen zur persistenten Homologie im maschinellen Lernen.
Persistente Homologie ist eine neuere Entwicklung in der angewandten algebraischen Topologie, die in verschiedenen Strategien des maschinellen Lernens verwendet wurde. In diesem Vortrag präsentieren wir eine kurze Einführung in dieses Thema mit mehreren Anwendungen in der Signalverarbeitung und Datenanalyse.

Marcus Venzke: Implementation von Neuronalen Netzen auf ressourcenbeschränkten Mikrocontrollern für den Einsatz in der Sensorik.
Der Workshop vermittelt, wie maschinelles Lernen mit künstlichen neuronalen Netzen (KNNs) in Sensormodulen mit preiswerten, leistungsschwachen Mikrocontrollern eingesetzt werden kann. Als Use-Case dient ein Sensormodul mit Lichtsensoren zur Erkennung einfacher Handgesten mit Mikrocontroller ATMega4809 (6 kB RAM, 20 MHz). Neben theoretischen Lehreinheiten enthält der Workshop viele praktische Demonstrationen zum Training und Einsatz von KNNs in den Sprachen Python (mit Keras / Tensorflow) und C (in Arduino-Entwicklungsumgebung).

Hidde Lekanne Deprez: Enhancing simulation images with GANs
In the Standard Platform League, certain types of annotations such as semantic segmentations, depth maps and object localization are difficult to obtain from real world recordings. The use of synthetic data could circumvent this problem as obtaining these annotations within a simulation is trivial. However, there is a catch, the reality gap makes algorithms trained on the synthetic images perform much worse in actual applications. Researchers can painstakingly implement more features to the simulation to close this gap. However, there are alternatives such as the neural networks presented here. The CycleGan and MUNIT architectures are able to make a domain translation, maintaining semantic information but changing the style, without any labels or matchings. This could mean that a translation between simulation and real images is possible as long as we have images of both domains. For my bachelor thesis I experimented with using these two neural networks to make this translation and my insights are presented in this talk.

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Merten Stender: Physics-Informed Learning
Machine Learning and Deep Learning have brought disruptive innovations to many fields since 2012. Today the application of those data-driven, and mostly black-box type models, can be regarded state-of-the-art in many scientific disciplines. However, the question of knowledge conservation arises: how to bring prior knowledge from generations of research and experience into the modeling process? This talk summarizes recent advances, lines of research and perspectives on “Physics-Informed Learning”, which is an umbrella term for blending first principles into evidence-based and data-driven models. Particular focus is put on engineering vibrations and spatio-temporal dynamics, e.g. water waves.

Daniel Höche: Towards predictive maintenance
Sustainable engineering requires reliable and plannable material behaviour in critical working environments like offshore. The extension of digital-twins towards virtual engineering assisted circular economy therefore needs computational models that enable the calculation of maintenance intervals or even the material condition at the end of its service life. The talk outlines how the combination of AI tools, data based models and physics based models facilitate predictive maintenance for metallic engineering materials exposed to severe conditions in-service. Aspects related to uncertainty, data availability or validation will be discussed.

Amir Kotobi: Dynamic structure investigation of biomolecules with pattern recognition algorithms and X-ray experiments
The biological functions of macromolecular systems, such as peptides and proteins, are largely defined by their spatial and electronic structures and thus it is of great importance to have high resolution view over these structures. Dynamic structure investigation of biomolecules with advanced molecular dynamic simulations and machine learning approaches on the basis of free energy calculations can give valuable opportunity in analysing the trajectories.

Prof. Date: Nihat Ay: Information Geometry for Deep Learning
In the first part of my presentation I will highlight the importance of the geometric perspective when dealing with learning systems. Information geometry offers a general framework for the identification of natural geometric structures for learning. The impact of this approach has been demonstrated in terms of the natural gradient method, one of the most prominent information-geometric methods within the field of machine learning. It was proposed by Amari in 1998 and uses the Fisher-Rao metric as a Riemannian metric for the definition of a gradient within optimisation tasks. Since then it proved to be extremely efficient in the context of neural networks, reinforcement learning, and robotics. However, training deep neural networks with this method remains a difficult task. I will present recent results that allow us to greatly simplify the natural gradient for deep learning. I will conclude my talk with an outline of further applications and extensions of information geometry that are particularly important for mathematical data science.

Prof. Date: Mirko Skiborowski: Automatic model development for the prediction of solvent flux and solute rejection in organic solvent nanofiltration
Affinity-based membrane separations offer a high potential for improving the energy efficiency of classical thermal separation processes. This is particularly true for pressure-driven membrane processes like organic solvent nanofiltration (OSN). However, OSN is rarely considered during conceptual process design, due to the absence of reliable models for quantitative predictions of the separation performance in different chemical systems. Commonly, a suitable membrane is first screened from a number of candidates in a series of initial experiments while a performance model of the most promising membrane is further derived for the specific application building on lab experiments and parameter regression for classical solution-diffusion or pore-flow models. Hence, the evaluation of OSN is usually requiring tedious experimental studies. In order to allow for an appropriate model-based assessment membrane-specific predictive models can be developed by means of an optimization-based data-driven approach. For this purpose a combination of genetic programming with deterministic global optimization for parameter regression and identifiability analysis is proposed, which automatically identifies suitable model structures and parametrization. For flux and rejection models for OSN, different descriptors that account for physical and chemical properties of the solutes and the solvents are correlated to experimentally measured data of different solutes and solvents. The resulting models allow for a quantitative analysis of the main performance metrics of the specific membrane for a certain chemical system as well as the most interesting application ranges on the basis of the decisive molecular descriptors that show a significant impact according to the derived model.

Prof. Date: Benedikt Kriegesmann: Efficient uncertainty quantification using surrogate models: application to fiber composite structures
Uncertainty quantification in engineering sciences takes into account the uncertainties that may exist and affect a certain physical system in an a priori unknown manner. If the input parameters of a system or model are subject to stochastic scatter, then also the output parameters (i.e. objective values) scatter randomly. The stochastic distribution of an objective value can be determined with uncertainty quantification methods such as Monte Carlo simulations. This requires a multitude of evaluations of the underlying model (up to 103, 106). Hence, this approach is infeasible for computationally demanding models. For such applications, surrogate models, like artificial neural networks, Kriging and polynomial chaos expansions, can be first trained with a small amount of model evaluations and then used as a proxy instead of the expensive model.
In the current talk, this procedure is demonstrated by the example of fiber composite structures. Here, the random objective functions are the strength and the stiffness of a component. Random input parameters are (amongst others) material properties, geometric deviations and manufacturing defects. Some random input parameters can only be modelled on a smaller scale than the whole component. Therefore, surrogate-boosted Monte Carlo simulations are performed on different scales and the results in each case are propagated to the higher scale. Strength however can hardly be approximated with standard surrogate models. Here, hierarchical surrogate models are used, which link models of different fidelity, to still allow for an efficient and accurate prediction of the stochastic distribution of strength properties.

Prof. Date: Matthias Mnich: Reinforcement Learning for Integer Programming
The integer programming problem is one of the most fundamental problems in combinatorial optimization, where one seeks an optimal solution among a finite, but usually extremely large set of discrete alternatives. Integer programs are ubiquitous in engineering and industrial applications, as they can model a large variety of highly complex tasks by means of discrete variables which are tight together through constraints. Powerful commercial solvers exists, which can solve large-scale instances with thousands of variables generally quite fast, but there are still several important integer programming models which cannot be solved at all. Theoretical and design and analysis of integer programming algorithms usually fails to explain both the successes, and the failures, of industrial solvers, as worst-case run times of those algorithms are often super-exponential in the number of variables. We discuss novel approaches based on methods of reinforcement learning to attack some of the most prominent integer programming models for which classical methods suffer from expensive time and memory usage, talking about their advantages and limitations.

Frederic E. Bock: Data-driven Machine Learning Corrections of Physics-Based Analytical Model Predictions towards High-Fidelity Simulation Solutions – a Hybrid Modelling Approach
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Lennart Bargsten: Deep learning approaches for intravascular ultrasound image processing: dealing with data scarcity.
Intravascular ultrasound (IVUS) plays a major role in clinical practice when it comes to assessing vessel morphologies during percutaneous coronary interventions (PCIs) or for treatment planning. Usually, the physician estimates morphological features by marking important regions in multiple IVUS images. This is a rather time consuming task and the results depend heavily on the physician’s experience. Automated detection and segmentation of meaningful image content can thus help streamlining the clinical workflow. Data driven methods like deep learning have gained huge importance in the field of medical image analysis over the last years. The usual scarcity of annotated image data in the medical field makes it important to tailor deep learning methods with respect to specific tasks and imaging modalities. Possibilities are generating synthetic image data, considering specific image characteristics as well as performing multi-task learning. This talk presents such approaches for improving deep learning performance on IVUS image analysis.

Morten Schierholz: Data Sources for Machine Learning Applications in Engineering.
Investigating the possibility of machine learning tools and techniques in the domain of engineering is a widely found concept with an increased number of authors and increasing audience. This development in the machine learning community has been led by researchers from data science. However this development has been possible due to large amount of data that has been available to these researchers in combination with an increased number of computational resources. For example the object recognition on images or in speech would not have been progressed as quickly without the widely available images over the internet. Generating and sharing knowledge is a broadly established concept in engineering domain, in form of conferences or other publications. However it is observed that in the domain of data the sharing of those is not established. First data sources are available where data is shared, however larger projects are not observed. We therefore propose a new database to share data and knowledge of this data. Thereby enabling researches that do not have the possibility to create those data samples by themselves, to work with interesting and rich data and apply new tools and techniques of the machine learning domain or others. Besides the data that is available in the database and the investigated machine learning tools and techniques on this data, a general overview of the database is given.

Marvin Kastner: How to Talk About Machine Learning with Jupyter Notebooks.
In the field of Machine Learning, scientists often use programming for data preprocessing, running the learning algorithms, and obtaining key metrics. To increase transparency, nowadays more and more additional material (such as datasets, code, documentation etc.) is shared so that fellow researchers can replicate these experiments. Jupyter Notebooks are a very valuable medium in this context – they are capable of displaying documentation, code, its output (such as visualizations, tables or logging messages) etc. side by side. Recently, Jupyter Notebooks have also been used in university courses more often. Here, the students benefit from the integration of code, its documentation, and the related exercise questions into a single interactive document. There are plenty of options how to design very appealing exercises for a course. Both in the scenario of transparent science and when using Jupyter Notebooks for teaching, the author’s code is meant to be run at another machine and achieve the same results. During this talk, possible issues during replication and suitable fixes are highlighted. The open source application JupyterHub can be part of that strategy. While the backend of the Integrated Development Environment runs on TUHH resources, the frontend is just a simple browser application. This reliefs the students from having up-to-date equipment for replication. Especially in times of COVID-19 this allows students to program from home more easily.

Thomas Kohlsche: Data-driven construction of fast predictors for complex systems using Gaussian process regression techniques.
In many real life applications, engineers are often interested in accessible predictions of a complex systems behavior for which only sparse data is available. The data may arise either from measurements or numerical simulations. Especially for numerical design tasks like optimization and uncertainty quantification where a very large number of model evaluations is required, cheap predictors, often called meta- or surrogate models, are unavoidable. Since gathering data from such systems is typically very costly, this task requires machine learning techniques that are capable of operating on small data sets. For the problem described, Gaussian process regression has proved itself to be a powerful and flexible tool. New extensions of the technique to, e.g., data-fusion concepts can offer solutions to problems that are hard to tackle with other techniques.

Benjamin Boll: Weakbond Detection – Enhancing Vibro-Acoustic Modulation Analysis by Machine Learning.
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Lars Köttner, Jan Mehnen, Denys Romaneko: Prozessüberwachung mit maschinellem Lernen für semi-automatisches Bohren von Nietlöchern in der Luftfahrtindustrie.
Die meisten der mehreren hundert Millionen gebohrten Flugzeugnietbohrungen pro Jahr werden mit semi-automatischen und manuell gesteuerten, pneumatisch angetriebenen Maschinen hergestellt, da eine Vollautomatisierung aufgrund von Arbeitsraumbeschränkungen oft ungeeignet ist. Diese Maschinen sind im Hinblick auf die Anpassbarkeit der Bohrungsparameter unflexibel, was u.a. auf die hohen Qualitätsanforderungen der Luftfahrtbranche zurückzuführen ist. Um zuverlässige und sichere Nietverbindungen herzustellen, ist das Bohren in mehreren Prozessschritten, der Einsatz von Minimalmengenschmierung sowie anschließendes manuelles Entgraten und Reinigen der Bohrungen unabdingbar. Vor diesem Hintergrund ermöglichen neu entwickelte elektrisch angetriebene semi-automatische Advanced Drilling Units (ADUs) neue Potenziale, wie z.B. intelligente Prozesslayouts, eine Online-Zustandsüberwachung durch die Auswertung integrierter Sensordaten und eine automatische Anpassung der Prozessparameter. Für die Zustandsüberwachung des Bohrprozesses wurde der Einsatz von maschinellem Lernen (ML) überprüft, um Schnittkräfte und Prozessbedingungen auf der Grundlage der Ströme der verwendeten Elektromotoren der ADUs vorherzusagen. Die Anwendung von ML auf ADU- Daten ist vorteilhaft, da die hohe Anzahl zu fertigender Nietbohrungen große Datensätze liefert. ML-Methoden wie lineare Regression, künstliche neuronale Netze, Entscheidungsbäume und k-Nearest-Neighbor wurden hinsichtlich ihrer Anwendbarkeit bewertet. Bohrprozesseigenschaften wie Material- und Vorschubgeschwindigkeitsauswahl wurden mit den Modellvorhersagen verglichen. Die vorgestellten Ergebnissen zeigen, dass eine ML-basierte Prozessüberwachung das Potenzial besitzt Prozessabweichungen zuverlässig zu identifizieren und auf diese Weise manuelle Nacharbeit zu reduzieren, eine umfassende Qualitätssicherung zu gewährleisten und die Ausnutzung der Werkzeugstandzeiten zu erhöhen.

Christian Feiler: Exploring Chemical Space using Computational Techniques.
The degradation behaviour of magnesium (Mg) renders it one of the most versatile engineering materials available today as it can be employed in a large variety of applications ranging from automotive and aerospace components to battery applications where Mg is used as anode material. However, a prerequisite to unlock the full potential of Mg–based materials is gaining control over the corrosion rate. Consequently, bespoke additives must be identified for each application for an optimal performance. Corrosion prevention is essential in transport applications to avoid material failure whereas constant dissolution of the material is required to boost the efficiency of Mg-air primary batteries. Furthermore, the search for benign and efficient corrosion inhibitors has become critical due to the imminent ban of highly effective but toxic chromates. The vast number of small molecules with potentially useful dissolution modulating properties (inhibitors or accelerators) renders conventional experimental discovery methods too time- and resource-consuming. Consequently, computer-assisted selection prior to experimental investigations of the most promising candidates is of great benefit in the search for efficient corrosion modulating additives for Mg-based materials. One of the major challenges is the identification of sound molecular descriptors that correlate well with experimentally derived properties as input parameters with low or no relevance to the modelled property will degrade the model. Towards this end, we utilized colour-coded correlation maps to facilitate an intuitive screening for reliable input features. We recently illustrated the potential of complementary quantum chemical density functional theory (DFT) calculations and machine learning methods for the prediction of corrosion modulating properties of small organic molecules for commercially pure Mg (CP Mg). Furthermore, we developed a workflow that facilitates screening of a large commercial database for an unbiased selection of untested additives for the control of the degradation rate of Mg.

Rupert Angerbauer: Anwendung von Methoden des Maschinellen Lernens zur Darstellung komplexer Strömungsfelder.
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Pascal Gleske, Konrad Nölle, Hendrik Sieck (HULKs): Distributed Genetic Neural Network Architecture Search at HULKs.
The increasing application of artificial neural networks (ANN) in various domains of robotics demands highly optimized ANN architectures. The efficient architecture search requires horizontal and vertical scaling of ANN evaluation. In this talk the HULKs present their approach and application of a scalable genetic algorithm based on distributed task execution, to be used in the context of RoboCup soccer competitions.

Leo Dostal: Towards Reinforcement Learning-based Control of an Energy Harvesting Pendulum.
Harvesting energy from the environment, e. g. ocean waves, is a key capability for the long-term operation of remote electronic systems where standard energy supply is not available. Rotating pendulums can be used as energy converters when excited close to their eigenfrequency. However, to ensure robust operation of the harvester, the energy of the dynamic system has to be controlled. In this study, we deploy a lightweight reinforcement learning algorithm to drive the energy of an Acrobot pendulum towards a desired value. We analyze the algorithm in an extensive series of simulations. Moreover, we explore the real world application of our energy-based reinforcement learning algorithm using a computationally constrained hardware setup based on low-cost components, such as the Raspberry Pi platform.

Leo Dostal: Advances in the development of car simulation models for vibration load prediction using Machine Learning.
Nowadays electric cars are a focus area in automotive research. In this context we consider data based approache as tools to improve and facilitate the car design process. Hereby, we address the challenge of vibration load prediction for electric cars using neural network based machine learning (ML), a data-based frequency response function approach, and a hybrid combined model. We extensively study the challenging case of vibration load prediction of car components, such as the traction battery of an electric car. We show using experimental data from a 1:5 scale model car as well as data from a Fiat 500e car that the proposed ML approach is able to outperform the classical model estimation by means of ARX and ARMAX models. Moreover, we evaluate the performance of a hybrid-ML concept for combination of ML and ARMAX. Our promising results motivate further research in the field of vibration load prediction using machine learning based approaches in order to facilitate design processes.

Helge Grossert: A Hybrid Approach for Modeling of Multibody Systems with Nonlinear Force Elements.
Due to the introduction of simplifications and idealizations during the modeling process of a real-world system, the created mathematical model will always behave slightly different compared to the real-world system. This might become problematic, depending e.g. on the use case of the model or the size of the deviation itself. In such a case, more complex models might produce relief, even though they cannot ensure satisfactory results. Furthermore, such modeling is not always possible, e.g. due to a lack of information about the real world system. In this talk, an approach for solving that kind of problems is presented. By inserting neural networks into the model created before, it is possible to reduce the deviation between model and real-world system without the need of more information except the measured data that is used to compare the model and the real-world system. The approach is presented by comparison of different modeling approaches of a nonlinear single mass oscillator.

Daniel Schoepflin: Bilddatengenerierung als Trainingsdatensatz für eine KI-Objektidentifikation in der Intralogistik.
Der massive Bedarf an Daten zum Training von neuronalen Netzwerken stellt den industriellen Transfer erforschter Ansätze vor hohe Herausforderungen. Frei verfügbare Datensätze sind oftmals nicht in der Lage, die spezifischen und individuellen Anforderungen von Unternehmen abzudecken. Die synthetische Erzeugung von Trainingsdaten zeigt sich hierbei als erfolgsversprechende Alternative. In diesem Vortrag wird die Generierung von Trainingsbildern für eine KI-Objektidentifikation im intralogistischen Umfeld beleuchtet und aufgezeigt, welche Hürden für eine erfolgreiche Implementierung genommen werden müssen.

Tobias Knopp, Görschwin Fey, Robert Meissner: tbd.
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Kai Sellschopp, Tim Würger: Exploration of structure - property relationships with unsupervised ML.
Many areas of applications – ranging from corrosion engineering to catalysis on inorganic surfaces and from drug design to polymer composites in organic materials – are influenced by the atomic structure of the materials involved. Luckily, due to modern experimental and simulation methods, it is often possible to obtain a detailed atomistic understanding of the achieved material properties. However, as the sheer number of potentially useful agents and their huge space of possible configurations renders comprehensive analyses resource- and time-consuming, other measures to predict the performance of yet untested molecules are required. One potential approach is the investigation of quantitative structure-property relationships (QSPR) using the Smooth Overlap of Atomic Positions (SOAP) kernel - a descriptor for atomic environments, that provides a translationally and rotationally invariant representation and therefore allows to calculate molecular similarities. Plotting these similarities on a map and combining them with experimental and theoretical results can then be used to intuitively explore structure-property relationships and predict yet unknown material properties.
In our talk, we first explain the basics of the SOAP kernel and how it can be used to distinguish atomic structures and speed up the process of finding the most favorable configurations. Then we show how SOAP is used in real life applications, such as in the control of magnesium-electrolyte interface properties, to gain deeper insights into fundamental mechanisms on an atomistic level.

kellner, Merten Stender: Explainable AI and its application to ice mechanics.
Criticism of data based models evolves around the problem of causation/correlation and the lack of knowledge generation when using those models. Naturally, and particularly for large black box models, this criticism is strongly connected to discussions under the umbrella of explainable or interpretable AI (XAI). After all, understanding why a model makes a prediction is key for, among others, trust, accountability, debugging and generalizability. A lack of understanding impedes improvement of models and input data as well as insight into the process being modeled.
To begin with, we give a general motivation and overview on interpretability of data based models. This includes why interpretability is important, possible perspectives on interpretability, and lastly interpretability-related methods and tools.
Secondly, we show-case machine learning and the SHAP (SHapley Additive exPlanation) interpretability toolbox to understand and predict the behavior of ice under compressive loads. Specifically, we are not interested in the best model but in which features drive model predictions, e.g. in a feature importance ranking. The identification of these features will be used as an addition to domain knowledge to create better material models for ice using a large experimental data base.

bahnsen: Emulation von neuronalen Netzen unter Hardwarefehlern.
Neueste Errungenschaften in verschiedenen Bereichen werden durch den Einsatz künstlicher neuronaler Netze (NN) erzielt, z.B. im Bereich der Spracherkennung oder der Bildverarbeitung. Ein NN löst Probleme durch statistisches Lernen mit ressourcenlastigen Berechnungen. Um NN für mobile Geräte, eingebettete oder IoT-Systeme zu implementieren, wird Hardwarebeschleunigung immer wichtiger, um Energie-, Kosten- oder Rechenzeitanforderungen zu erfüllen. In einem Hardwarebeschleuniger werden die arithmetischen Operationen des NN sequentiell auf wenigen Recheneinheiten berechnet, so dass ein Fehler in der Verarbeitungshardware einen erheblichen Einfluss auf die Ausgabe des NNs haben kann. Die Zuverlässigkeit eines NNs und damit auch der zugehörigen Anwendung hängt dann nicht mehr ausschließlich von statistischen Fehlern im NN-Modell ab - die Zuverlässigkeit wird vielmehr durch das Zusammenspiel von NN-Modell und Hardware bestimmt. In dem Vortrag wird eine Technik zur Emulation von NN-Inferenz auf Hardware-Ressourcenbeschreibungen erläutert. Anschließend werden die Injektion von Hardwarefehlern und deren Auswirkung an verschieden Beispielen erörtert.

Tobias Knopp, Görschwin Fey, Robert Meißner: tbd.
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Morten Schierholz: Application of Artificial Neural Networks for Power Integrity Evaluation.
Increasing demands on modern electronic systems with respect to Signal and Power Integrity on printed circuit boards require many simulations during an optimization process. The necessity of additional and more complex simulations requires new and advanced simulation and optimization techniques. Using machine learning is one attempt to improve the efficiency of these optimization processes. The high dimensional problem of the power integrity analysis is especially challenging. The approach to improve the power delivery network of printed circuit boards with decoupling capacitors is analysed with artificial neural networks. The focus is based on the importance of preprocessing the input data and exploit the available domain knowledge to increase the accuracy of the artificial neural network.

Sebastian Lindner: Predictive medium access techniques for wireless networks.
Spectrum scarcity requires novel approaches for sharing frequency resources between different radio systems. Where coordination is not possible, intelligent approaches are needed, allowing a novel “secondary” system to access unused resources of a legacy (primary) system without requiring modifications of this primary system. Machine Learning is a promising approach to recognize patterns of the primary system and adapt the channel access accordingly. In this contribution we investigate the capability of Feed-Forward Deep Learning and Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) to detect communication patterns of the primary user.
Therefore, we take the example of a new aeronautical system (LDACS) coexisting with three different systems. Firstly, the coexistence with the Distance Measurement Equipment (DME) providing a deterministic interference to the secondary user and secondly with two synthetic channel access patterns, realized by a 2-state Markov model, modeling a bursty channel access behavior, as well as through a sequential channel access model.
It can be shown that the Markov property of a Gilbert-Elliot channel model limits the predictability; nonetheless, we show that the model characteristics can be fully learned, which could leverage the design of interference avoidance systems that make use of this knowledge. The determinism of DME allows an error-free prediction, and it is shown that the reliability of sequential access model prediction depends on the model’s parameter.
The limits of Feed-Forward Deep Neural Networks are highlighted, and why LSTM RNNs are state-of-the-art models in this problem domain. We show that these models are capable of online learning, as well as of learning correlations over long periods of time.

stark: End-to-end learning in Wireless Communications.
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Rüdiger Schmitz: tbd.
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Niklas Jahn: ML-unterstützte Problembeschreibungen in digitalen Assistenzsystemen.
Im industriellen Umfeld ermöglichen Augmented-Reality-Anwendungen die Erstellung am Bauteil dreidimensional verorteter Rückmeldungen. Diese dokumentieren mit kurzen Texten und Fotos Montageprobleme und Bauteilfehler in der Produktion. Allerdings schwankt die Informationsqualität der Rückmeldungen in Abhängigkeit des Erstellers und dessen Zeit zur Eingabe der Beschreibungen auf dem mobilen Endgerät. Auf maschinellem Lernen basierende Empfehlungsdienste bieten einem Nutzer die unterstützende Möglichkeit, Vorschläge für sinnvolle Textbausteine einer Problembeschreibung zu erhalten.
Ich werde einen Prototyp für einen dafür geeigneten hybriden Empfehlungsdienst vorstellen, welcher sich aus einer Bildklassifikation mittels Deep Learning und einer Textverarbeitung mittels Data Mining und Natural Language Processing zusammensetzt.
Einen weiteren Schwerpunkt stellt die Integration des Prototyps in Form eines ML-Microservices in eine Cloud-Infrastruktur am Beispiel von Kubernetes und Python-Webservices dar, sodass dieser von externen Anwendungen genutzt und gleichzeitig leicht weiterentwickelt werden kann.