Dr. Alexander Kovacs, MSc BSc

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

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

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

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

Nieves, P.; Arapan, S.; Maudes-Raedo, J.; Marticorena-Sánchez, R.; Del Brío, N. L.; Kovacs, A.; Echevarria-Bonet, C.; Salazar, D.; Weischenberg, J.; Zhang, H.; Vekilova, O. Yu.; Serrano-López, R.; Barandiaran, J. M.; Skokov, K.; Gutfleisch, O.; Eriksson, O.; Herper, H. C.; Schrefl, T.; Cuesta-López, S. (2019). Database of Novel Magnetic Materials for High-Performance Permanent Magnet Development. Computational Materials Science, 168: 188-202

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

Exl, L.; Fischbacher, J.; Kovacs, A.; Oezelt, H.; Gusenbauer, M.; Yokota, K.; Shoji, T., Hrkac, G.; Schrefl, T.; (2018). Magnetic microstructure machine learning analysis. JPhys Materials, 2: 014001/https://doi.org/10.1088/2515-7639/aaf26d

Fischbacher, J.; Kovacs, A.; Exl, L.; Kühnel, J.; Mehofer, E.; Sepehri-Amin, H.; Ohkubo, T.; Hono, K.; Schrefl, T. (2018). Searching the weakest link: Demagnetizing fields and magnetization reversal in permanent magnets. Scripta Materialia, 154: 253/https://doi.org/10.1016/j.scriptamat.2017.11.0

Fischbacher, J.; Kovacs, A.; Gusenbauer, M.; Oezelt, H.; Exl, L.; Bance, S.; Schrefl, T. (2018). Micromagnetics of rare-earth efficient permanent magnets. Journal of Physics D: Applied Physics, Vol. 51, no. 19: 193002-193019

Mitin, D.; Kovacs, A.; Schrefl, T.; Ehresmann, A.; Holzinger, D.; Albrecht, M. (2018). Magnetic properties of artificially designed magnetic stray field landscapes in laterally confined exchange-bias layers. Nanotechnology, Vol. 29: 355708

Fischbacher, J.; Kovacs, A.; Exl, L.; Kühnel, J.; Mehofer, E.; Sepehri-Amin, H.; Ohkubo, T.; Hono, K.; Schrefl, T. (2017). Searching the weakest link: Demagnetizing fields and magnetization reversal in permanent magnets. Scripta Materialia, in press: in press

Fischbacher, J.; Kovacs, A.; Oezelt, H.; Gusenbauer, M.; Schrefl, T.; Exl, L.; Givord, D.; Dempsey, N. M.; Winklhofer, M.; Hrkac, G.; Chantrell, R.; Sakuma, N.; Yano, M.; Kato, A.; Shoji, T.; Manabe, A. (2017). On the limits of coercivity in permanent magnets. Applied Physics Letters, 111(7): 072404