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WEBINAR ENDED
Fri, Dec 4, 2020 · 9:30 AM · 2 hours

AI for Automotive Manufacturing and Mobility-as-a-Service

Fri, Dec 4, 2020 · 9:30 AM · CET
About This Webinar

Welcome to the 1st Webinar of the AI4DI project!
During this Webinar, experts from the relevant fields will present the goals and results of the ECSEL-JU co-funded project AI4DI, and the progress in the automotive and transportation domains. Predictive maintenance, 2nd life battery usage in automotive industries, learning and training in simulation, and other AI-related use cases will be presented.
During the Webinar, the attendees will have the opportunity to ask questions and comment on any issue of their interest. Join us to discuss the future with AI!

Who can view: Everyone
Webinar Price: Free
Featured Presenters
Webinar hosting presenter
Reiner John is Director of R&D projects at Infineon Technologies. He is the initiator and coordination of a number of large international R&D projects for electro mobility, renewable energies, energy efficiency, and artificial intelligence. Reiner John is the coordinator of AI4DI, as well as a number of other finalised and ongoing projects: 3Ccar, AutoDrive, Silverstream, OSEM-EV, Luftstrom, 1000KMplus, E3Car, CASTOR, BATMAN, Motorbrain, and others.
Webinar hosting presenter
Dr. Daniel Plorin has a doctorate in systems engineering in factory planning and factory operation and is employed at AUDI AG in the central digitization unit of production. He is responsible for the initial operationalization of digitization projects in the group. Since studying systems engineering, he finished in 2012 and completed a doctorate at the chair of factory planning and factory operation at the Technical University of Chemnitz. In his research, he dealt with the versatile factory, the use of new technologies and the digital as well as learning factory.
In 2016 and after various industrial projects in the automotive and other industrial sectors, he joined AUDI AG as a specialist in digitization and area management. A key focus here is on the further development of artificial intelligence technologies and their integration into series processes.
Webinar hosting presenter
Dr. Matti Kutila leads the Automated Vehicles Team in VTT. His career started in 1998, first as a researcher and later as a Project Manager of the automotive industry related R&D projects. He obtained Master of Science degree in 2000 and completed his driver monitoring and neural networks related doctoral thesis in 2006. Recent years his expertise fields have been focused on ITS sensor solutions, V2X technologies, sensor data fusion and artificial intelligence.
Webinar hosting presenter
Technology Futurist
Webinar hosting presenter
Noah Klarmann received the Master of Science degree in Chemical Engineering at the Technical University of Berlin in 2014. Afterwards, he started as a research assistant at the Chair of Thermodynamics at the Technical University of Munich in 2015. During this time, he developed novel modeling strategies for turbulent combustion in the context of computational fluid dynamics. He received a PhD (summa cum laude) under the supervision of Prof. Dr.-Ing. Thomas Sattelmayer in 2019. In parallel to the doctoral program, Noah Klarmann started to study Computer Science with focus on AI at Technical University of Munich in 2017. In 2019, he joined the Chair of Robotics, Artificial Intelligence and Real-time Systems as a post-doctoral researcher. In his role, he is part of the AI4DI project that focuses on the training of digital twins of industrial robots in virtual environments.

Abstract of Webinar talk: The imminent convergence of artificial intelligence with the industry is one of the most interesting topics of our time. Adopting new methods such as large-scale machine learning has the power to fundamentally change industrial processes and will pave the way for new business models. While the adoption of supervised and unsupervised methods is widespread in many sectors, the paradigm of reinforcement learning is often overlooked. This does not reflect the impressive progress in this area, such as the defeat of world champions in several disciplines without prior domain knowledge (AlphaGo, OpenAI Five, AlphaStar). The talk introduces the exciting field of reinforcement learning as a powerful tool to solve complex sequential decision-making problems in the industrial context. Beyond the discussion of general concepts, two possible applications of reinforcement
learning for industrial applications are presented: (1) Continuous control of industrial manipulators and (2) dynamic production scheduling. Both use cases are examined by employing simplified simulation environments that allow to study basic characteristics.
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