
Dipl.-Ing.(FH) Dr. Markus Gusenbauer
Wissenschaftlicher Projektmitarbeiter - Zentrum für Modellierung und Simulation
- markus.gusenbauer@donau-uni.ac.at
- +43 2732 893-5405
- Zum Kontaktformular
- Campus Krems, Trakt C, 2. Stock, 2.210
- Universität für Weiterbildung Krems
- Zentrum für Modellierung und Simulation
- Dr.-Karl-Dorrek-Straße 30
- 3500 Krems
- Österreich
Projekte (Auszug Forschungsdatenbank)
Magnetism at interfaces: from quantum to reality
Towards the digital twin of a permanent magnet
The Effect of Interfaces on Magnetisation Reversal in MnAl-C
Publikationen (Auszug Forschungsdatenbank)
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
Zhao, P.; Gusenbauer, M.; Oezelt, H.; Wolf, D.; Gemming, T.; Schrefl, T.; Nielsch, K.; Woodcock, T. G. (2023). Nanoscale chemical segregation to twin interfaces in t -MnAl-C and resulting effects on the magnetic properties. Journal of Materials Science & Technology, Vol. 134: 22-32
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
Zhao, P.; Gusenbauer, M.; Oezelt, H.; Wolf, D.; Gemming, T.; Schrefl, T.; Nielsch, K.; Woodcock, T. G. (2022). Nanoscale chemical segregation to twin interfaces in t-MnAl-C and resulting effects on the magnetic properties. Journal of Materials Science & Technology, Vol. 134: 22-32
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
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
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
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.; 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
Gusenbauer, M.; Mazza, G.; Posnicek, T.; Brandl, M.; Schrefl, T. (2018). Magnetically actuated circular displacement micropump. The International Journal of Advanced Manufacturing Technology, 95: 3575/https://doi.org/10.1007/s00170-017-1440-5
Gusenbauer, M.; Schrefl, T. (2018). Simulation of magnetic particles in microfluidic channels. Journal of Magnetism and Magnetic Materials, Volume 446: 185-191
Gusenbauer, M.; Tothova, R.; Mazza, G.; Brandl, M.; Schrefl, T.; Jancigova, I.; Cimrak, I. (2018). Cell Damage Index as Computational Indicator for Blood Cell Activation and Damage. Artificial Organs, Volume 42, Issue 7: 746-755
Vorträge (Auszug Forschungsdatenbank)
Coercivity analysis of twin boundaries with demagnetization negligible models in arbitrary field direction
JEMS 2022, 26.07.2022
Machine Learning for Relating Structure and Coercivity of Permanent Magnets
Virtual REPM 2021, 09.06.2021
Bridging the gap between biomedical applications and material sciences
3rd Workshop on Modelling of Biological Cells, Fluid Flow and Microfluidics, 11.02.2020
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
Automated micromagnetic simulations from Electron Backscatter Diffraction data
JEMS 2018, 05.09.2018
Sensing the blood cell damage in a magnetically actuated circular pump
IEEE Sensors 2017, 01.11.2017
Model-Based Design and Optimization of Microfluidic Systems for Gentle Cellular Perfusion
Sensor2017 Nürnberg, 31.05.2017
Immersed magnetic objects in biological fluids
2nd Workshop on Modelling of Biological Cells, Fluid Flow and Microfluidics, Vrátna, Slovakia, 08.02.2017
Keep the blood cells happy
2nd Workshop on Modelling of Biological Cells, Fluid Flow and Microfluidics, Vrátna, Slovakia, 06.02.2017
Rapid prototyping of miniature blood vessels
2nd Workshop on Modelling of Biological Cells, Fluid Flow and Microfluidics, Vrátna, Slovakia, 06.02.2017
Cell rheology in microfluidic perfusion: computational and experimental approach
MNE 2016, 21.09.2016
Simulation of magnetic particles in blood flow to improve failsafe particle detection of microspheres based detoxification system
Particles 2015, 28.09.2015
Automated microfluidic optimization to reduce blood cell activation
CFD in Medicine and Biology II, 01.09.2015
Dynamics of magnetic particles in microfluidic channels
ICNAAM 2015, 23.09.2014