Dr.-Ing. Richard Meyes, M.Sc.
Scientific Researcher
Head of the Research Field "Interpretable Learning Models"
Area of Research:
- Artificial Intelligence and Machine Learning for Industrial Appliations
- Predictive Analysis of Time Series Data in Industrial Sensor Systems
- Structured Representations in Artificial Neural Networks
Biography
Dr.-Ing. Richard Meyes has been a research associate at the Institute for Technologies and Management of Digital Transformation at the University of Wuppertal since December 2018. His research focuses on the development and investigation of artificial intelligence methods, with a focus on artificial neural networks, in various application fields, including automotive and manufacturing.
Publications
- 2024
- Hahn, Y., Kienitz, P., Wönkhaus, M., Meyes, R., & Meisen, T. (2024). "Towards Accurate Flood Predictions: A Deep Learning Approach Using Wupper River Data" , Water , 16 (23),
- 2023
- Hahn, Y., Langer, T., Meyes, R., & Meisen, T. (2023). "Time Series Dataset Survey for Forecasting with Deep Learning" , Forecasting , 5 (1), 315—335.
- Bulow, F., Hahn, Y., Meyes, R., Meisen, T., & others, (2023). "Transparent and Interpretable State of Health Forecasting of Lithium-Ion Batteries with Deep Learning and Saliency Maps" , International Journal of Energy Research , 2023 ,
- Langer, T., Welbers, V., Hahn, Y., Wönkhaus, M., Meyes, R., & Meisen, T. (2023). "Visual Interactive Exploration and~Labeling of~Large Volumes of~Industrial Time Series Data" in Enterprise Information Systems , Filipe, Joaquim and Śmiałek, Michał and Brodsky, Alexander and Hammoudi, Slimane, Eds. Cham : Springer Nature Switzerland 85—108.
ISBN: 978-3-031-39386-0
- 2022
- Langer, T., Meyes, R., & Meisen, T. (2022). "Gideon Replay: A library to replay interactions in web-applications" , SoftwareX , 17 , 100964.
