Superalloy Data Science
Acquisition, Storage, Mining and Machine Learning
International symposium, 30./31. January 2020, Ruhr University Bochum, Germany
The symposium is devoted to the data science of single crystal Ni- and Co-base superalloys. The central objective is to review interdisciplinary aspects of superalloy data science, to identify areas in need of development and to explore the potential of machine learning tools. The symposium will establish the state of the art in superalloy data science, identify available data and discuss all aspects associated with data acquisition, data storage and data mining of heterogeneous research data. Emphasis will be placed on how to apply machine learning concepts and material informatics in advanced superalloy technology.
The symposium is organized by Thomas Hammerschmidt (ICAMS, RUB) Gunther Eggeler (IFM, RUB) and Uwe Glatzel (Metals and Alloys, University Bayreuth). The symposium is supported by the Collaborative Research Centre SFB/TR 103 (From Atoms to Turbine Blades) of the German Research Association (DFG) and the Materials Research Department of the Ruhr University Bochum.
- Antonin Dlouhy, IPM, CZAS, Brno, CZE
- Chris Eberl, Fraunhofer IWM, Freiburg, Germany
- Luca Ghiringhelli, Fritz Haber Institute, Berlin, Germany
- Tilmann Hickel, MPIE, Düsseldorf, Germany
- Surya R. Kalidindi, Georgia Inst. of Technology, USA
- Catherine M.F. Rae, University of Cambridge, UK
- Krishna Rajan, University of Buffalo, USA
- Stefanie Reese, RWTH Aachen, Germany
- Stefan Sandfeld, TU Freiberg, Germany
- Yunzhi Wang, Ohio State University, USA