Dipl.-Ing.(FH) Johann Fischbacher, MSc
- johann.fischbacher@donau-uni.ac.at
- +43 2622 23420-57
-
+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
Publications (Extract Research Database)
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
Brueckl, H.; Breth, L.; Fischbacher, J.; Schrefl, T.; Kuehrer, S.; Pachlhofer, J.; Schwarz, M.; Weirather, T.; Czettl, C. (2024). Machine learning based prediction of mechanical properties of WC-Co cemented carbides from magnetic data only. International Journal of Refractory Metals and Hard Materials, Vol. 121: 106665
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
de Moraes, I. G.; Fischbacher, J.; Hong, Y.; Naud, C.; Okuno, H.; Masseboeuf, A.; Devillers, T.; Schrefl, T.; Dempsey, N. M. (2024). Nanofabrication, characterisation and modelling of soft-in-hard FeCo–FePt magnetic nanocomposites. Acta Materialia, Vol. 274: 119970
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
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
Fischbacher, J.; Schrefl, T.; Moraes, I.; Dempsey, N. (2023). Micromagnetic modelling of soft-in-hard FeCo-FePt nanocomposites. 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
Ali, Q.; Fischbacher, J.; Kovacs, A.; Oezelt, H.; Gusenbauer, M.; Moustafa, H.; Böhm, D.; Breth, L.; Schrefl, T. (2023). Defect Manipulation for the Coercivity Enhancement of Nd-Fe-B Permanent Magnets. SSRN, 2023: 4628986, Elesevier
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
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
Lectures (Extract Research Database)
Micromagnetic study of the impact of grain boundaries on coercivity
REPM2023, 06/09/2023
Micromagnetic modelling of soft-in-hard FeCo-FePt nanocomposites
13th International Symposium on Hysteresis Modeling and Micromagnetics (HMM 2023), 05/06/2023
Magnetization reversal of large granular magnetic materials
HMM 2023, 05/06/2023
Magnetic Hardening of Neodymium-lean Permanent Magnets by Local Replacement of Grains by High Anisotropy Phases
Intermag 2023, 16/05/2023
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
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
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