Noah Klarmann is a Research Professor of Artificial Intelligence at Rosenheim University of Applied Sciences since 2021. His teaching and research areas include topics such as the use of Artificial Intelligence for technical systems and digital ethics.
Prof. Dr.-Ing. 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 under the supervision of Prof. Dr.-Ing. Thomas Sattelmayer in 2019 (summa cum laude). In parallel to the doctoral program, Noah Klarmann started to study Computer Science with focus on Artificial Intelligence at Technical University of Munich in 2017. In 2019, he joined the Chair of Robotics, Artificial Intelligence and Real-time Systems at Technical University of Munich as a post-doctoral researcher. In this role, he was part of the AI4DI project that focuses on the training of digital twins of industrial robots in virtual environments. Moreover, Noah Klarmann was part of the coordination team of the EU-Project SHOP4CF.
Noah Klarmann is a research professor of Artificial Intelligence at Rosenheim University of Applied Sciences since 2021. His teaching and research areas include topics such as the use of Artificial Intelligence for technical systems and digital ethics. The research group has a strong focus on production and is closely linked to the proto_lab, an I4.0 platform for research and development.
Member of TH Rosenheim’s Science Board (Wissenschaftsausschusses der TH Rosenheim)
Member of the Ethical Council of Bavarians Universities for Applied Sciences (Gemeinsame Ethikkommission der Hochschulen Bayerns - GEHBa)
Digital Ethics (DigEt)
Programming for Data Science (ProDS)
Data Science (DaSci)
Autonomous Guided Vehicles for Smart Industries (AGVSI)
Reinforcement Learning for Technical Systems and Production (RLTSP)
Reinforcement learning (e.g., self-learning control, robotics, or production scheduling)
Supervised learning (e.g., predictive models – how will the product quality be?)
Unsupervised learning (e.g., anomaly detection)
Computer vision (based on hand-crafted algorithms or machine learning)
Time series analysis (based on hand-crafted algorithms or machine learning)
Cloud technologies (e.g., cloud manufacturing)
Mixed Reality (e.g., remote maintenance with augmented reality)
Digital Ethics (e.g, privacy & GDPR, machine ethics)
2024
Schneevogt, M.; Binninger, K., Klarmann, N.: Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics. Preprint available on arXiv, 2024.
Mehta, D.; Klarmann, N.: Manufacturing Quality Control With Autoencoder-Based Defect Localization and Unsupervised Class Selection. Mach. Learn. Knowl. Extr.2024, 6(1), 1-17; https://doi.org/10.3390/make6010001
2023
Vivekanandan, D.; Wirth, S.; Karlbauer, P.; Klarmann, N.: A Reinforcement Learning Approach for Scheduling Problems with Improved Generalization through Order Swapping. Mach. Learn. Knowl. Extr.2023, 5, 418-430. https://doi.org/10.3390/make5020025
2022
Josifovski, J.; Malmir, M.; Klarmann, N.; Knoll, A.: Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks. IROS, 2022.
2022
Josifovski, J.; Malmir, M.; Klarmann, N.; Knoll, A.: Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks. IROS, 2022.
2021
Klarmann, N.: Reinforcement Learning für die Industrierobotik. Konzepte der Robotik - Technik in Bayer | VDI, 2021.
Klarmann, N.: Artificial Intelligence Narratives: An Objective Perspective on Current Developments. Preprint available on arXiv, 2021.
Klarmann, N.; Malmir, M., Josifovski, J.; Plorin, D.; Wagner, M., Knoll, A.: Optimising Trajectories in Simulations with Deep Reinforcement Learning for Industrial Robots in Automotive Manufacturing. The AI4DI Book, 2021.
Hofmeister, T.; Hummel, T.; Klarmann, N.; Sattelmayer, T.: Elimination of Numerical Damping in the Stability Analysis of Non-Compact Thermoacoustic Systems With Linearized Euler Equations. Journal of Engineering for Gas Turbines and Power 143 (3), 2021. (peer-reviewed)
2020
Klarmann, N.; Sattelmayer, T.: Canonical Validation of a Modeling Strategy for Carbon Monoxide Emissions in Staged Operation of Gas Turbine Combustors. Journal of the Global Power and Propulsion Society 4 (1),2020. (peer-reviewed)
Josifovski, J.; Malmir, M.; Klarmann, N.; Knoll, A.: Continual Learning on Incremental Simulations for Real-World Robotic Manipulation Tasks. 2nd Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics, 2020.
Malmir, M.; Josifovski, J.; Klarmann, N.; Knoll, A.: Robust Sim2Real Transfer by Learning Inverse Dynamics of Simulated Systems. 2nd Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics, 2020.
2019
Klarmann, N.: Modeling Turbulent Combustion and CO Emissions in Partially-Premixed Conditions Considering Flame Stretch and Heat Loss. PhD Thesis, Technical University of Munich, 2019.
Klarmann, N.; Zoller, B.; Sattelmayer, T.: Modeling of CO Emissions in Multi-Burner Systems with Fuel Staging. Proceedings of the ASME Turbo Expo, Phoenix, 2019. (peer-reviewed)
2018
Klarmann, N.; Zoller, B.; Sattelmayer, T.: Numerical Modeling of CO-Emissions for Gas Turbine Combustors Operating at Part-Load Conditions. Journal of the Global Power and Propulsion Society 2(1), 2018.(peer-reviewed)
2017
Klarmann, N.; Zoller, B.; Sattelmayer, T.: Numerical Modeling of CO-Emissions for Gas Turbine Combustors Operating at Part-Load Conditions. Proceedings of Global Power and Propulsion Forum, Shanghai, 2017. (peer-reviewed)
Schulze, M.; Hummel, T.; Klarmann, N.; Berger, F.; Schuermans, B.; Sattelmayer, T.: Linearized Euler Equations for the Prediction of Linear High-Frequency Stability in Gas Turbine Combustors. Journal of Engineering for Gas Turbines and Power 139 (3), 2017. (peer-reviewed)
2016
Klarmann, N.; Sattelmayer, T.; Weiqun, G.; Magni, F.: Flamelet Generated Manifolds for Partially-Premixed, Highly-Stretched and Non-Adiabatic Combustion in Gas Turbines. 54th AIAA Aerospace Sciences Meeting, San Diego,2016. (peer-reviewed)
Klarmann, N.; Sattelmayer, T.; Zoller, B.; Geng, W.; Magni, F.: Impact of Flame Stretch and Heat Loss on Heat Release Distributions in Gas Turbines Combustors: Model Comparison and Validation. Proceedings of the ASME Turbo Expo, Seoul, 2016. (peer-reviewed)
Klarmann, N.; Sattelmayer, T.; Zoller, B.; Magni, F.: Modellierung der CO-Bildung und -Oxidation in Mehrbrennersystemen mit Brennstoffstufung. Tagungsband der AG Turbo, 2016.
Schulze, M.; Hummel, T.; Klarmann, N.; Berger, F.; Schuermans, B.; Sattelmayer, T.: Linearized Euler Equations for the Prediction of Linear High-Frequency Stability in Gas Turbine Combustors. Proceedings of the ASME Turbo Expo, Seoul,2016. (peer-reviewed)
