The research project addresses social challenges in the area of tension between demographic change and digitization. The "baby boomer" generation accounts for around one fifth of the total population in Germany. As they gradually enter retirement age, municipalities are challenged to create age-appropriate care structures. The goal is to develop a system that serves public stakeholders as a decision-making aid in their planning processes using methods from artificial intelligence, software development and mathematical optimization. For this purpose, the Optimization working group (Mathematics) creates and examines complex models from route and location planning, among other things.

The project is funded by the Carl-Zeiss-Stiftung.

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The joint project AGENS deals with the development of flexible models based on neural networks, which are able to simulate realistic demand data of electricity consumers and thus contribute to an improvement of the data quality for each individual consumer. As the core of the project, so-called generative adversarial neural networks are developed for this purpose, which enable data augmentation. For a successful training, statistical pre-analyses of the data have to be performed in order to determine their characteristic patterns and to feed these into the neural networks. Based on the augmented data set, the goal is to subsequently be able to train robust models with calibrated uncertainties and to ensure their applicability in an industrial setting.

The project is funded by the Bundesministerium für Bildung und Forschung  (BMBF).

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In ever smaller pipelines, the wall properties have an increasing influence on the flow conditions. Thus, with the aid of applied coatings consisting of microstructures, wall properties and their flow influence can be brought about in a targeted manner. Air pockets enclosed within these functional structures reduce, for example, the flow resistance of a liquid flowing over them, according to the lotus effect. Application perspectives are, among others, miniaturized processes or microsystems. In contrast to previous concepts, the surfaces developed within the project promise better applicability as well as higher stability of the enclosed air. The research combines analytical modelling, numerical simulation, surface fabrication and experiments. The partner of the project is the Photonik-Zentrum Kaiserslautern.

This project is funded by the Deutsche Forschungsgemeinschaft (DFG).

In the DAnoBi project, methods for the detection of anomalies in large image data are being developed. The focus is on the detection of cracks in concrete structures. For this purpose, machine learning methods, stochastic modeling of structures and imaging, and statistical methods are combined for the detection of anomalies. The high variability of the microstructure of concrete requires the development of methods and measures to decide objectively, robustly and repeatably what is an anomaly, i.e. a significant deviation. Partners of this project are the Universität Ulm and the Otto-von-Guericke-Universität Magdeburg.

This project is funded by the Bundesministerium für Bildung und Forschung (BMBF).

Machine learning methods are being used in more and more areas of our world. There are also opportunities for industry, for example in the optimization of products or the monitoring of industrial processes. Chemical processes and their plants are highly complex and produce huge amounts of heterogeneous time series data that are recorded by various sensors at different measuring points. Even the most experienced plant operators cannot keep track of all this. Safety is the top priority. If the process runs into a critical area, it must be shut down in time if necessary. However, this is usually very expensive. Unnecessary shutdowns must therefore be avoided. The task now is to identify critical conditions early and reliably. Humans alone cannot do this. Partners of this project are the TU München and the Universität Oldenburg.

This project in funded by the Deutsche Forschungsgemeinschaft (DFG).

EASIER (sEAmless SustaInable EveRyday urban mobility) is an interdisciplinary, transnational research project, funded for 3 years. The goal of EASIER is to increase the share of active and sustainable personal transport modes, such as walking, biking, public transport and shared mobility services, and to integrate them to appealing multimodal journeys. The subproject „Efficient tariff systems for sustainable mobility“ at RPTU aims to use tariff systems actively to increase the demand and share of public transport. Therefore, tariff systems and pricing strategies for integrated mobility systems are analyzed and developed, focussing on passenger behavior, their route choice and the underlying line network.

The project is funded by the Bundesministerium für Bildung und Forschung (BMBF) via ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC).

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The meniscus regeneration and involved cell and tissue-level phenomena pose a difficult biomedical problem. Clinical studies indicate that partial and total meniscectomies lead to prevalence of premature osteoarthritis in knee joints. Therefore, substantial efforts are being made towards finding adequate regenerative tissue for meniscus replacement. Mathematical modelling, simulation and experimental validation can provide basic control mechanisms in cell-scaffold interactions under different environmental parameters, and yield a selective prognosis of the most significant combinations of these parameters. For the application of in-silico modelling and simulation a challenging part is the well-posed and numerically efficient coupling of the processes at the cell level with the macroscopic behaviour and the mechanical properties of the tissue. Partners of the projekt are the DITF Denkendorf and the Universität Ulm.

The project is funded by the DFG as part of the program SSP 2311.

The HALF project enters the next round. The goal of the BMBF pilot innovation project "Energy-efficient AI systems" was the development of energy-efficient hardware that enables artificial intelligence for the evaluation of cardiac arrhythmias on mobile devices. For this purpose, a cross-layer approach was developed in the HALF project in collaboration with Fraunhofer ITWM, which enables a Pareto-optimal hardware implementation including an automated neural network search. The project was awarded 1st place in the FPGA category in this competition by the Federal Minister of Education and Research in March 2021, and was awarded a new BMBF project in which a new ECG method for the automated mobile cardiac arrhythmia detection was developed, which has a 5x longer runtime with simultaneously improved detection accuracy compared to the state of the art.

This project is funded by the  Bundesministerium für Bildung und Forschung (BMBF).

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There are essentially two different paradigmatic approaches for mapping complex physical processes: classical physical modeling with associated numerical simulation (model-based) and prognostic methods based on the analysis of large amounts of data (data-driven). In recent years, the efficient combination of data-driven and model-based approaches has become a research topic in its own right. The aim of the present joint project is to integrate data-driven and model-based approaches into an overall solution on the basis of a continuum mechanics problem from the automotive industry that has so far been insufficiently understood. This is intended to decisively improve the computer-aided mapping of the associated process.

The project is funded by the Bundesministerium für Bildung und Forschung (BMBF).

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The innovation platform KEEN connects 20 industrial and scientific institutions with the aim of introducing artificial intelligence technologies and methods in the process industry. As part of KEEN, the research team is working on the development of hybrid predictive substance data models. Substance data are of fundamental importance in process engineering, but since their experimental determination is expensive and time-consuming, predictive methods are needed. While physical methods are well established for this purpose, data-driven machine learning methods open up completely new perspectives. In the project, both worlds are brought together and superior hybrid approaches, which combine the expressivity of machine learning methods with the extensive existing physical knowledge, are developed.

The project is funded by the Bundesministerium für Wirtschaft und Energie  (BMWi).

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The nanostructure of complex materials can be imaged in 3D by the FIB-SEM serial sectioning technique. To analyse the material, its components must be reconstructed from the image data. This is difficult in the case of high porosity, since structures behind the current sectional area are also visible through the pores. Machine learning techniques have high potential here. However, training data are difficult to obtain. Manual segmentation is hardly possible, since also humans often cannot decide which structures lie in the plane which has just been cut. Synthetic images for which the correct result is known are an attractive solution. In this approach, the similarity between the simulated and the real structure significantly influences the quality of the result. Partner of this project is the DFKI Saarbrücken.

The project is funded by the Bundesministerium für Bildung und Forschung (BMBF).

Quantum technologies are pushing ever more strongly and rapidly into industrial application. The development is so fast in some areas that the demand for specialists cannot be met by graduates of universities. To enable working professionals to qualify in quantum technologies at an academic level, the RPTU Kaiserslautern-Landau (RPTU) will develop a part-time, interdisciplinary distance learning course. The German Federal Ministry of Education and Research (BMBF) is providing around two million euros over three years for the conception and development of this course as part of the funding programs for quantum technologies. Under the leadership of Prof. Artur Widera, we are working in mathematics together with colleagues from the departments of electrical engineering and information technology, computer science, and physics, as well as the Fraunhofer ITWM and the DFKI, on new teaching content and formats to enable optimized distance learning at the Distant and Independent Studies Center (DISC) at RPTU. The new master's program, called QuanRPTU, will be launched in three years at the Distance and Independent Studies Center (DISC) of the RPTU. It will be located at the interface between physics, mathematics, computer science, and electrical engineering and information technology.

This project is funded by the Bundesministerium für Bildung und Forschung  (BMBF).

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The connection of CAD methods and finite elements was established in the last decade by isogeometric analysis (IGA for short). Here, the calculation areas are often defined by their edges. This is exactly where Scaled-Boundary Isogeometric Analysis (SB-IGA) comes in, which deals with the parameterization of domains based on NURBS boundary surfaces. The project, which is carried out in cooperation with the Chair of Structural Dynamics at RWTH Aachen University (Prof. Sven Klinkel), focuses on the coupling of different computational domains as well as the global smoothness of the underlying approach functions. Furthermore, the advantages of the SB-IGA approach in the context of "trimming", i.e. adding and removing regions in CAD, will be investigated. The theoretical considerations and methods will then be applied in the field of structural dynamics.

This project in funded by the Deutsche Forschungsgemeinschaft (DFG).

It is highly probable that quantum computers are able to decipher current encoding systems until the end of this decade. Regarding critical infrastructures like water supply, this can lead to danger of human lives. The goal of this project is to investigate new encoding systems that can compete against attacks from quantum computers.

In this project, the RPTU cooperates with researchers from the TU of Munich and the pump manufacturer KSB in order to develop new solutions for quantum computer resistant encoding systems for cloud based applications in the "Internet of Things". The main focus is put on an efficient implementation of procedures on embedded systems.

This project is funded by the Bundesministerium für Bildung und Forschung (BMBF).

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Machine learning is an artificial intelligence process that is currently in the process of changing almost all areas of our lives: from personal assistants to medicine and health to self-driving cars. ML systems are capable of extracting knowledge from large amounts of data, but they require high computational power and a lot of energy to do so.

The goal of this project is to develop methods that reduce the energy consumption of this technology so that it can also be applied to smaller control units of machines. To this end, basic machine learning methods are to be modified in such a way that the background knowledge of humans can be used to require less data and less computing power.  The project is coordinated by Professor Dr. Paul Lukowicz from the Department of Computer Science at the RPTU. The project partner is the German Research Center for Artificial Intelligence.

The project is funded by the Carl-Zeiss-Stiftung.

Artificial intelligence (AI) is used very successfully in image recognition, processing and understanding. However, training an AI-based inspection system for industrial quality assurance requires large amounts of representative annotated image data for all defect types. Manual annotation is laborious and error-prone. Many defects, especially safety-critical ones, occur very rarely. Realistic synthetic image data help circumvent these problems.   
We combine physics, mathematics and computer science to generate synthetic images of typical defects on metallic surfaces. These defect images that are guaranteed to be correctly and objectively annotated are available for training and validation of AI systems for optical surface inspection after the end of the project.

The project is funded by the Bundesministerium für Bildung und Forschung (BMBF).