Siemens Technology has been working on physical and industrial applications of neural networks and reinforcement learning for more than 20 years. With multiple deployments in various industrial domains (steel, paper, power plants, factory automation, mobility), we have learned about the challenges and constraints that are related to safety-critical environments and real-world applicability. Developing algorithms built on domain expertise, we solve reinforcement learning (RL) tasks with little data but lots of available expert knowledge, and need to establish data and machine learning pipelines as well as deployment strategies. As part of Siemens offerings, RL needs to be reliable, trustworthy, and cost-efficient.
In this talk, we will discuss RL use cases that might impact you every day. Starting with RL-controlled power plant gas turbines, we will introduce typical requirements from the industry and present derived research and software results.
As a dedicated applied researcher and physicist I started my scientific career at the international Dark Matter search experiment XENON. After Postdoc positions at MPIK Heidelberg and Columbia University in N.Y.C, I changed fields to join the Learning Systems groups at Siemens Technology where I have been driving applied machine learning topics and solving control optimization problems for the industrial domain using AI and reinforcement learning.
Volkmar Sterzing is over 30 years active in the Neural Network and Machine Learning field. Together with his research team at Siemens, he pioneered forecasting and industrial control applications of AI and reinforcement learning in various applications. In 2017 he was awarded inventor of the year at Siemens for the reinforcement learning based continuous gas turbine tuning. This application has now become a product of Siemens Energy. Volkmar heads the Research Group "Learning Systems" at Siemens Technology.