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)
Laufende Projekte
Towards the digital twin of a permanent magnet
Projektzeitraum: 01.02.2024–31.01.2027
Projektverantwortung (Universität für Weiterbildung Krems): Markus Gusenbauer
Fördergeber: Bundesländer (inkl. deren Stiftungen und Einrichtungen)
Magnetism at interfaces: from quantum to reality
Projektzeitraum: 01.11.2022–31.10.2025
Projektverantwortung (Universität für Weiterbildung Krems): Markus Gusenbauer
Fördergeber: FWF
Towards the digital twin of a permanent magnet
Projektzeitraum: 01.03.2022–28.02.2026
Projektverantwortung (Universität für Weiterbildung Krems): Markus Gusenbauer
Fördergeber: FWF
Abgeschlossene Projekte
The Effect of Interfaces on Magnetisation Reversal in MnAl-C
Projektzeitraum: 01.10.2017–30.11.2020
Projektverantwortung (Universität für Weiterbildung Krems): Markus Gusenbauer
Fördergeber: FWF
Förderprogramm: DACH
Publikationen (Auszug Forschungsdatenbank)
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
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
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
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
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
Vorträge (Auszug Forschungsdatenbank)
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
Multiscaling strategies in computational magnet design
Going Green – CARE INNOVATION 2023, 11.05.2023
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