Details
| Projektzeitraum | 01.03.2014 - 20.02.2016 |
|---|---|
| Fördergeber | Sonstige |
| Department |
Department für Rechtswissenschaften und Internationale Beziehungen |
| Projektverantwortung (Universität für Weiterbildung Krems) | Univ.-Prof. Dr. Dr. Thomas Ratka, LL.M. |
Publikationen
Gusenbauer, M.; Kovacs, A.; Özelt, H.; Fischbacher, J.; Zhao, P.; Woodcock, T.G.;Schrefl, T. (2021). Insights into MnAl-C nano-twin defects by micromagnetic characterization. Journal of Applied Physics, 129(9): 093902
Gusenbauer, G.; Oezelt, H.; Fischbacher, J.; Kovacs, A.; Zhao, P.; Woodcock, T. G.; Schrefl, T. (2020). Extracting local switching fields in permanent magnets using machine learning. npj Computational Materials, 6: 89ff
Arapan, S.; Nieves, P.; Cuesta-López, S.; Gusenbauer, M.; Oezelt, H.; Schrefl, T.; Delczeg-Czirjak, E. K.; Herper, H. C.; Eriksson, O. (2019). Influence of antiphase boundary of the MnAl t-phase on the energy product. Physical Review Materials, Vol. 3, iss. 6: 064412
Gusenbauer, M.; Fischbacher, J.; Kovacs, A.; Oezelt, H.; Bance, S.; Zhao, P.; Woodcock, T.G.; Schrefl, T. (2019). Automated meshing of electron backscatter diffraction data and application to finite element micromagnetics. Journal of Magnetism and Magnetic Materials, Volume 486: 165256
Vorträge
Machine Learning for Relating Structure and Coercivity of Permanent Magnets
Virtual REPM 2021, 09.06.2021
Bridging the gap between biomedical applications and material sciences
3rd Workshop on Modelling of Biological Cells, Fluid Flow and Microfluidics, 11.02.2020
Micromagnetic characterization of MnAl-C using trained neural networks
JEMS2019, Uppsala, Schweden, 29.08.2019
Automated micromagnetic simulations from Electron Backscatter Diffraction data
JEMS 2018, 05.09.2018