
Univ.-Doz.Dipl.-Ing.Dr. Thomas Schrefl
- thomas.schrefl@donau-uni.ac.at
- +43 2622 23420-20
- +43 2622 23420-99 (Fax)
- To contact form
- TFZ Wiener Neustadt, Section E - Floor 2
- University for Continuing Education Krems
- Center for Modelling and Simulation
- Viktor Kaplan Straße 2 - Bauteil E
- 2700 Wiener Neustadt
- Austria
Projects (Extract Research Database)
Simumag - GFF Horizon Europe Start-up funding
Magnet design through physics informed machine learning
Nanostructured multiphase permanent magnets
Nanostructured multiphase permanent magnets
Atomistic Simulation of rare-earth reduced permanent magnets
Publications (Extract Research Database)
Kovacs, A.; Fischbacher, J.; Oezelt, H.; Kornell, A.; Ali, Q.; Gusenbauer, M.; Yano, M.; Sakuma, N.; Kinoshita, A.; Shoji, T.; Kato, A.; Hong, Y.; Grenier, S.; Devillers, T.; Dempsey, N. M.; Fukushima, T.; Akai, H.; Kawashima, N.; Miyake, T.; Schrefl, T. (2023). Physics-Informed Machine Learning Combining Experiment and Simulation for the Design of Neodymium-Iron-Boron Permanent Magnets with Reduced Critical-Elements Content. Frontiers in Materials 2023, Vol. 9: 1-19
Yamano, H.; Kovacs, A.; Fischbacher, J.; Danno, K.; Umetani, Y.; Shoji, T.; Schrefl, T. (2023). Efficient optimization approach for designing power device structure using machine learning. Japanese Journal of Applied Physics, Vol. 1: 1-17
Zhao, P.; Gusenbauer, M.; Oezelt, H.; Wolf, D.; Gemming, T.; Schrefl, T.; Nielsch, K.; Woodcock, T. G. (2023). Nanoscale chemical segregation to twin interfaces in t -MnAl-C and resulting effects on the magnetic properties. Journal of Materials Science & Technology, Vol. 134: 22-32
Heistracher, P.; Abert, C.; Bruckner, F.; Schrefl, T.; Suess, D. (2022). Proposal for a micromagnetic standard problem: domain wall pinning at phase boundaries. Journal of Magnetism and Magnetic Materials, Vol. 548: 168875
Kovacs, A.; Exl, L.; Kornell, A.; Fischbacher, J.; Hovorka, M.; Gusenbauer, M.; Breth, L.; Oezelt, H.; Yano, M.; Sakuma, N.; Kinoshita, A.; Shoji, T.; Kato, A.; Schrefl, T. (2022). Conditional physics informed neural networks. Communications in Nonlinear Science and Numerical Simulation, Vol. 104: 106041
Kovacs, A.; Exlc, L.; Kornell, A.; Fischbacher, J.; Hovorka, M.; Gusenbauer, M.; Breth, L.; Oezelt, H.; Praetorius, D.; Suess, D.; Schrefl, T. (2022). Magnetostatics and micromagnetics with physics informed neural networks. Journal of Magnetism and Magnetic Materials, Vol. 548: 168951
Mohapatra, J.; Fischbacher, J.; Gusenbauer, M.; Xing, M. Y.; Elkins, J.; Schrefl, T.; Liu, J. P. (2022). Coercivity limits in nanoscale ferromagnets. Phys. Rev. B, Vol. 105, Iss. 21: 214431
Oezelt, H.; Qu, L.; Kovacs, A.; Fischbacher, J.; Gusenbauer, M.; Beigelbeck, R.; Praetorius, D.; Yano, M.; Shoji, T.; Kato, A.; Chantrell, R.; Winklhofer, M.; Zimanyi, G.; Schrefl, T. (2022). Full- Spin-Wave-Scaled Stochastic Micromagnetism for Mesh-Independent Simulations of Ferromagnetic Resonance and Reversal. npj Computational Materials, Vol. 8: 35
Zhao, P.; Gusenbauer, M.; Oezelt, H.; Wolf, D.; Gemming, T.; Schrefl, T.; Nielsch, K.; Woodcock, T. G. (2022). Nanoscale chemical segregation to twin interfaces in t-MnAl-C and resulting effects on the magnetic properties. Journal of Materials Science & Technology, Vol. 134: 22-32
Cuadrado, R.; Evans, R. F. L.; Shoji, T.; Yano, M.; Kato, A.; Ito, M.; Hrkac, G.; Schrefl, T.; Chantrell, R.W. (2021). First principles and atomistic calculation of the magnetic anisotropy of Y2Fe14B. JOURNAL OF APPLIED PHYSICS, 130: 023901
Ener, S.; Skokov, K. P.; Palanisamy, D.; Devillers, T.; Fischbacher, J.; Eslavac, G.; Maccaria, F.; Schäfer, L.; Diop, L.; Radulov, I.; Gault, B.; Hrkac, G.; Dempsey, N.; Schrefl, T.;Raabe, D.; Gutfleisch, O. (2021). Twins – A weak link in the magnetic hardening of ThMn12-type permanent magnets. Acta Materialia, Vol. 214: 116968
Exl, L.; Mauser, N. J.; Schaffer, S.; Schrefl, T.; Suess, D.; (2021). Prediction of magnetization dynamics in a reduced dimensional feature space setting utilizing a low-rank kernel method. JOURNAL OF COMPUTATIONAL PHYSICS, 444: 110586
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
Perna, S.; Schrefl, T.; Serpico, C.; Fischbacher, J.; Del Pizzo, A. (2021). Microstructure Role in Permanent Magnet Eddy Current Losses. IEEE TRANSACTIONS ON MAGNETICS, 57: 6300405
Tsuchiura, H.; Yoshioka, T.; Novák, P.; Fischbacher, J.; Kovacs, A.; Schrefl, T. (2021). First-principles calculations of magnetic properties for analysis of magnetization processes in rare-earth permanent magnets". Science and Technology of Advanced Materials (STAM), Vol. 22, no. 1: 748-757
Exl, L.; Mauser, N.; Schrefl, T.; Suess, D. (2020). Learning time-stepping by nonlinear dimensionality reduction to predict magnetization dynamics. Communications in Nonlinear Science and Numerical Simulation, Vol. 84: 105205
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
Kovacs, A.; Fischbacher, J.; Gusenbauer, M.; Oezelt, H.; Herper, H. C.; Vekilova, O. Y.; Nieves, P.; Arapan, S.; Schrefl, T. (2020). Computational design of rare-earth reduced permanent magnets. Engineering, 6: 148
Schönhöbel, A.M.; Madugundo, R.; Barandiarán, J.M.; Hadjipanayis, G.C.; Palanisamy, D.; Schwarz, T.; Gault, B.; Raabe, D.; Skokov, K.; Gutfleisch, O.; Fischbacher, J.; Schrefl, T. (2020). Nanocrystalline Sm-based 1:12 magnets. Acta Materialia, Vol. 200: 652-658
Skelland, C.; Westmoreland, S.C.; Ostler, T.; Evans, R.F.L.; Chantrell, R.W.; Yano, M.; Shoji, T.; Kato, A.; Ito, M.; Winklhofer, M.; Zimanyi, G.; Schrefl, T.; Fischbacher, J.; Hrkac, G. (2020). Atomistic study on the pressure dependence of the melting point of NdFe12. AIP Advances, Vol. 10, iss. 2: 025130
Lectures (Extract Research Database)
Generative deep learning for permanent magnet microstructures
67th Annual Conference on Magnetism and Magnetic Materials (MMM 2022), 03/11/2022
How to Create an Effective Scientific Video Presentation
67th Annual Conference on Magnetism and Magnetic Materials (MMM 2022), 02/11/2022
Materials Informatics for the Design of Rare-Earth Reduced Permanent Magnets
Magnetic Materials and Applications 22, 26/10/2022
Magnetization processes in SmFeO3
DPG Frühjahrstagung, 06/09/2022
Machine Learning Analysis of Multiphase Magnetic Microstructures
CIMTEC 2022, 23/06/2022
Physics informed neural networks for computational magnetism
MMM-Intermag 2022, 10/01/2022
Inverse design of Nd-substituted permanent magnets
Physics and the green economy, 25/11/2021
Tutorial: An introduction to machine learning for solving micromagnetic problems
The 2021 Around-the-Clock Around-the-Globe Magnetics Conference, 24/08/2021
Deep learning magnetization dynamics
IEEE Advances in Magnetism 2021, 16/06/2021
New trends for machine learning in permanent magnet design
The 26th International Workshop on Rare Earth and Future Permanent Magnets and Their Application, 10/06/2021
Machine Learning for Relating Structure and Coercivity of Permanent Magnets
Virtual REPM 2021, 09/06/2021
Machine learning, micromagnetics and magnet design
University of York, Computational Magnetism, 02/12/2020
Finding weak spots in permanent magnets through micromagnetism and machine learning
CMD2020GEFES, 02/09/2020
Computational Design of Bulk Permanent Magnet
TMS2020, 25/02/2020
Bridging the gap between biomedical applications and material sciences
3rd Workshop on Modelling of Biological Cells, Fluid Flow and Microfluidics, 11/02/2020
Advancing permanent magnets by machine learning
Meeting of CRC/TRR 270 - Hysteresis design of magnetic materials for efficient energy conversion, 05/02/2020
Computer based optimization of permanent magnets
Seminar, CEA, Grenoble, 17/12/2019
Learning Magnetization Dynamics
64th Annual Conference on Magnetism and Magnetic Material, Las Vegas, USA, 07/11/2019
Machine learning for permanent magnet optimization
2019 - Sustainable Industrial Processing Summit & Exhibition, Paphos, Cryprus, 26/10/2019
Micromagnetic optimization of permanent magnetic materials
27th International Conference on Materials and Technology, Portoroz, Slovenia, 17/10/2019