Projects in the Business Area Numerical Data-Driven Prediction

ManuBrain – universal, scalable AI-platform for industrial applications

In the »ManuBrain« project, a universal, scalable, and open platform for artificial intelligence applications in medium-sized industrial companies is being developed. The state of North Rhine-Westphalia and the European Fund for Regional Development are supporting the project over three years with a total of 1.8 million euros. Fraunhofer SCAI develops and evaluates machine learning methods for engineering applications.
Project duration: 01/2020 - 12/2022

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ViPrIA – Virtual Product Development Using Intelligent Assistance Systems

The goal of the project ViPrIA is the development of intelligent assistance systems based on artificial intelligence and machine learning approaches to support engineers in simulation-based, virtual product development. With the help of intelligent assistance functions, calculation engineers are to be supported in the development process with complex decisions and relieved of routine tasks. ViPrIA is funded by the BMBF.
Project duration: 10/2019 - 09/2022

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EVOLOPRO

The big vision of Industry 4.0 is the automatic adaptation of production processes to rapidly changing requirements. The new Fraunhofer Leitprojekt EVOLOPRO wants to come a step closer to this vision. The project is part of the Fraunhofer initiative "Biological Transformation". Starting in 2019, seven institutes will jointly investigate how developmental and evolutionary biological principles can be transferred to man-made production processes. Transfer and multi-task learning play an important role here - as well as the concept of so-called "digital twins". Biology is the great role model for the work in EVOLPRO and provides important impulses for the further development of procedures.
Project duration: 01/2019 - 12/2023

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KI Lernlabor

Entering and further training in artificial intelligence

The "Artificial Intelligence Learning Lab" is being set up at the Fraunhofer Institute Center Schloss Birlinghoven in Sankt Augustin. It supports small and medium-sized enterprises (SMEs) in particular in getting started in artificial intelligence (AI), learning about concrete applications, and qualifying employees.
Project duration: 11/2019 - 04/2022

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EXCELLERAT

New analysis methods improve evaluation of complex engineering data

A further increase in the performance of supercomputers is expected over the next few years. So-called exascale computers will then be able to deliver more accurate simulation results. Fraunhofer SCAI is developing efficient data analysis methods for the much larger amounts of data generated in this way, which will also provide the engineer with detailed insights into the complex technical interrelationships.
Project duration: 12/2018 - 11/2021

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MADESI

Using machine learning for the early detection of anomalies helps to avoid damages

The analysis of sensor data of machines, plants or buildings makes it possible to detect anomalous states early and thus to avoid further damage. For this purpose, the monitoring data is searched for anomalies. By means of machine learning, anomaly detection can already be partially automated.
Project duration: 10/2018 - 09/2021

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MathEnergy - Mathematical Key Technologies for Energy Networks

The project MathEnergy develops mathematical aproaches to tackle the challenges which arise in the context of the energy turnaround. In order to adapt the workload and the expansion of the energy networks, offers and demands have to be adjusted and flexibilities among the energy sources have to be utilized. For this purpose, network-transcending models and model-based monitoring, controlling and evaluation concepts are developed. The goal is to develop a software package for hierarchical complex network models which supports stochastically varying input data and workflows for the integrated simulation and analysis of network-transcendent scenarios of the energy supply with power and gas.
Project duration: 10/2016 - 09/2020
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Flex4Apps

The Flex4Apps project addresses the challenges of monitoring and optimizing large, distributed, cyber-physical systems. The goal of the project is to provide a solution to manage the high data volumes and complexity of system monitoring whilst disturbing the target system as little as possible.
Project duration: 11/2016 - 10/2019

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