Thomas Schrefl

Projekte (Auszug Forschungs­datenbank)

Publikationen (Auszug Forschungs­datenbank)

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

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

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

Tang, X.; Li, J.; Miyazaki, Y.; Sepehri-Amin, H.; Ohkubo, T.; Schrefl, T.; Hono, K. (2020). Relationship between the Thermal Stability of Coercivity and the Aspect Ratio of Grains in Nd-Fe-B Magnets: Experimental and Numerical Approaches. Acta Materialia, 183: 408-417

Westmoreland, S. C.; Skelland, C.; Shoji, T.; Yano, M.; Kato, A.; Ito, M.; Hrkac, G.; Schrefl, T.; Evans, R.; Chantrel, R. W. (2020). Atomistic simulations of a-Fe/Nd2Fe14B magnetic core/shell nanocomposites with enhanced energy product for high temperature permanent magnet applications. AIP, Vol. 127: 133901

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

Dirba, I.; Li, J.; Sepehri-Amin, H.; Ohkubo, T.; Schrefl, T.; Hono, K. (2019). Single-Crystalline SmFe12-Based Microparticles with High Roundness Fabricated by Jet-Milling. Journal of Alloys and Compounds, 804: 155-162

Exl, L.; Fischbacher, J.; Kovacs, A.; Oezelt, H.; Gusenbauer, M.; Schrefl, T. (2019). Preconditioned nonlinear conjugate gradient method for micromagnetic energy minimization. Computer Physics Communications, 235: 179-186

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

Kovacs, A.; Fischbacher, J.; Gusenbauer, M.; Oezelt, H.; Herper, H. C.; Vekilova, O. Yu.; Nieves, P.; Arapan, S.; Schrefl, T. (2019). Computational Design of Rare-Earth Reduced Permanent Magnets. Engineering, November 2019: in press

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Vorträge (Auszug Forschungs­datenbank)

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

Computational optimization of permanent magnets

Ruhr Symposium 2019, Duisburg, Germany, 09.10.2019

Modelling of microstructure for optimum hard magnetic properties

MMA’19: Magnetic Materials and Applications, Milano, Italy, 18.09.2019

Micromagnetic characterization of MnAl-C using trained neural networks

JEMS2019, Uppsala, Schweden, 29.08.2019

Ferromagnetic resonance simulations for stochastic Landau-Lifshitz-Gilbert equation

The Joint European Magnetic Symposia (JEMS), Uppsala, Sweden, 29.08.2019

Bridging the gap between academic software and industry needs - different business models for engaging with industry

EMMC Workshop, Cambridge, UK, 21.05.2019

Microstructure optimization for rare-earth efficient permanent magnets

DPG Spring Meeting, Regensburg, Germany, 01.04.2019

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