Dr. Alexander Kovacs, MSc BSc

Sandoval, C.; Miguel, A.; Jurczyk, J.; Skoric, L.; Sanz-Hernández, D.; Leo, N.; Kovacs, A.; Schrefl, T.; Hierro-Rodríguez, A.; Fernández-Pacheco, A. (2025). Remote-sensing based control of 3D magnetic fields using machine learning for in operando applications. Journal of Applied Physics, Vol. 137, no 11: 113905-1 - 113905-7

Ali, Q.; Fischbacher, J.; Kovacs, A.; Özelt, H.; Gusenbauer, M.; Moustafa, H.; Böhm, D.; Breth, L.; Schrefl, T. (2024). Defect manipulation for the coercivity enhancement of Nd-Fe-B permanent magnets. Physica B: Condensed Matter, Vol. 678: 415759

Gusenbauer, M.; Stanciu, S.; Kovacs, A.; Oezelt, H.; Fischbacher, J.; Zhao, P.; Woodcock, T. G.; Schrefl, T.; Stanciu S. (2024). Micromagnetic study of grain junctions in MnAl-C containing intergranular inclusions. Elsevier Journal of Magnetism and Magnetic Materials, Vol. 606: 172390

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. (2024). Image-based prediction and optimization of hysteresis properties of nanocrystalline permanent magnets using deep learning. Journal of Magnetism and Magnetic Materials, Vol. 596: 171937

Moustafa, H.; Kovacs, A.; Fischbacher, J.; Gusenbauer, M.; Ali, Q.; Breth, L.; Hong, Y.; Rigaut, W.; Devillers, T.; Dempsey, N. M.; Schrefl, T.; Özelt, H. (2024). Reduced Order Model for Hard Magnetic Films. AIP Advances, Vol. 14, iss. 2: 025001-1 bis 025001-5

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

Breth, L.; Schrefl, T.; Fischbacher, J.; Oezelt, H.; Kovacs, A.; Czettl, C.; Pachlhofer, J.; Schwarz, M.; Brueckl, H. (2023). Micromagnetic simulations as a tool for bottom-up explainability of FORC diagrams. Proceedings in AIM IEEE Advances in Magnetics 2023, Vol. 1: 1

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

Okabe, R.; Li, M.; Iwasaki, Y.; Regnault N.; Felser, C.; Shirai, M.; Kovacs, A.; Schrefl, T.; Hirohata, A. (2023). Materials Informatics for the Development and Discovery of Future Magnetic Materials. IEEE Magnetics Letters, vol. 14: 1-5

Schaffer, S.; Schrefl, T.; Oezelt, H.; Kovacs, A.; Breth, L.; Mauser, N.J.; Suess, D.; Exl, L. (2023). Physics-informed machine learning and stray field computation with application to micromagnetic energy minimization. Journal of Magnetism and Magnetic Materials, 576: 170761

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

Ali, Q.; Fischbacher, J.; Kovacs, A.; Oezelt, H.; Gusenbauer, M.; Yano, M.; Sakuma, N.; Kinoshita, A.; Shoji, T.; Kato, A.; Schrefl, T. (2023). Benchmarking for systematic coarse-grained micromagnetics. In: HMM, proceedings in 13th International Symposium on Hysteresis Modeling and Micromagnetics (HMM 2023): 1, HMM, WIen

Gusenbauer, M.; Oezelt, H.; Kovacs, A.; Fischbacher, J.; Zhao, P.; Woodcock, T.-G.; Schrefl, T. (2023). Magnetization reversal of large granular magnetic materials. In: HMM, proceedings in 13th International Symposium on Hysteresis Modeling and Micromagnetics (HMM 2023): 1, HMM, Wien

Kovacs, A.; Fischbacher, J.; Oezelt, H.; Ali, Q.; Gusenbauer, M.; Schrefl, T. (2023). Finite Hex Element Adaptive Mesh Refinement of Demagnetizing Field Computation. In: HMM, proceedings in 13th International Symposium on Hysteresis Modeling and Micromagnetics (HMM 2023): 1, HMM, Wien

Oezelt, H.; Kovacs, A.; Breth, L.; Gusenbauer, M.; Schaffer, S.; Exl, L.; Schrefl. T. (2023). Machine learning based optimization of hard-/soft magnetic nanostructures. In: HMM, proceedings in 13th International Symposium on Hysteresis Modeling and Micromagnetics (HMM 2023): 1, HMM, Wien

Wager, C.; Kovacs, A.; Schrefl, T. (2023). Active Learning Scheme vs Conventional Optimization - developing a Python Framework. In: HMM, proceedings in 13th International Symposium on Hysteresis Modeling and Micromagnetics (HMM 2023): 1, HMM, Wien

Breth, L.; Fischbacher, J.; Kovacs, A.; Oezelt, H.; Schrefl, T.; Czettl, C.; Kuehrer, S.; Pachlhofer, J.; Schwarz, M.; Weirather, T.; Brueckl, H. (2023). Structural and micromagnetic modeling of the magnetic binder phase in WC-Co cemented carbides. IEEE International Magnetic Conference - Short Papers, 2023: https://doi.org/10.1109/INTERMAGShortPapers58606.2023.10304872

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