Johann Fischbacher

Publikationen (Auszug Forschungs­datenbank)

Breth, L.; Fischbacher, J.; Kovacs, A.; Özelt, H.; Schrefl, T.; Brückl, H.; Czettl, C.; Kührer, S.; Pachlhofer, J., Schwarz, M. (2023). FORC diagram features of Co particles due to reversal by domain nucleation. Journal of Magnetism and Magnetic Materials 571 (2023) 170567 Available online 24 February 2023 0304-8853/© 2023 Elsevier B.V. All rights reserved.Contents lists available at ScienceDirect Journal of Magnetism and Magnetic Materials, Vol. 571: 1-6

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

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

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

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

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

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

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

Kovacs, A.; Fischbacher, J.; Oezelt, H.; Gusenbauer, M.; Exl, L.; Bruckner, F.; Suess, D.; Schrefl, T. (2019). Learning Magnetization Dynamics. Journal of Magnetism and Magnetic Materials, 491: 165548

Skelland, C.; Ostler, T.; Westmoreland, S.C.; Evans, R.F.L.; Chantrell, R.W.; Yano, M.; Shoji, T.; Kato, A.; Winkelhofer, M., Zimanyi, G.; Fischbacher, J.; Schrefl, T.; Hrkac, G. (2019). The Effect of Interstitial Nitrogen Addition on the Structural Properties of Supercells of NdFe12-xTix. IEEE Transactions on Magnetics, Vol. 55, iss. 10: 6700205

Vekilova, O. Y.; Fayyazi, B.; Skokov, K. P.; Gutfleisch, O.; Echevarria-Bonet, C.; Barandiarán, J. M.; Kovacs, A.; Fischbacher, J.; Schrefl, T.; Eriksson, O.; Herper, H. C. (2019). Tuning the Magnetocrystalline Anisotropy of Fe3Sn by Alloying. Physical Review B, 99: 024421

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Vorträge (Auszug Forschungs­datenbank)

Magnetization reversal of large granular magnetic materials

HMM 2023, 05.06.2023

Multiscaling strategies in computational magnet design

Going Green – CARE INNOVATION 2023, 11.05.2023

Machine Learning for Relating Structure and Coercivity of Permanent Magnets

Virtual REPM 2021, 09.06.2021

Ferromagnetic resonance simulations for stochastic Landau-Lifshitz-Gilbert equation

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

Micromagnetic characterization of MnAl-C using trained neural networks

JEMS2019, Uppsala, Schweden, 29.08.2019

Micromagnetic simulation of surface anisotropy effects in SmFe_12-type permanent magnets

JEMS2019 Joint European Magnetic Symposia, Uppsala, Schweden, 26.08.2019

Micromagnetic simulation of surface anisotropy effects in SmFe12 type permanent magnets

Joint European Magnetic Symposia, Uppsala, Sweden, 26.08.2019

Micromagnetic Simulation of Partially Ordered L1_0 FeNi Permanent Magnets

MANA 2018 - Micromagnetics: Analysis, Numerics, Applications, TU Wien, Resselgasse 4, 1040 Wien, Österreich, 08.11.2018

Micromagnetic Simulation of Partially Ordered L1_0 FeNi Permanent Magnets

21st International Conference on Magnetism (ICM2018) Moscone Center, San Francisco, USA, 16.07.2018

Micromagnetic Simulation of Partially Ordered L1_0 FeNi Permanent Magnets

Santorini Workshop Future Perspectives on Novel Magnetic Materials, Santorini Palace Hotel, Santorini, Griechenland, 30.05.2018

AF-10-Influence of Alignment Depenent Grain Boundary Composition Changes on Coercivity and Magnetic Reversal of Nd2Fe14B Megnets

Konferenz MMM 2017 in Pittsburgh, 07.11.2017

Self demagnetizing effects in NdFe12 based magnets at high temperature

61st Annual Conference on Magnetism and Magnetic Materials, New Orleans, Louisiana, 31.10.-4.11.2016, 03.11.2016

Magnetization reversal in Dy-diffused permanent magnets

61st Annual Conference on Magnetism and Magnetic Materials, New Orleans, Louisiana, 31.10.-4.11.2016, 02.11.2016

Improving coercivity in shape anisotropy based permanent magnets

MMM Intermag 2016 Joint Conference, 11.-15. Januar 2016, San Diego, Kalifornien, 14.01.2016

Potential of NdFe12Nx based permanent magnets

MMM Intermag 2016 Joint Conference, 11.-15. Januar 2016, San Diego, Kalifornien, 13.01.2016

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