Dipl.-Ing.(FH) Dr. Markus Gusenbauer
- markus.gusenbauer@donau-uni.ac.at
- +43 2732 893-5405
- To contact form
- Campus Krems, Tract C, 2nd Floor, 2.210
- University for Continuing Education Krems
- Center for Modelling and Simulation
- Dr.-Karl-Dorrek-Straße 30
- 3500 Krems
- Austria
Projects (Extract Research Database)
Running projects
Towards the digital twin of a permanent magnet
Duration: 01/02/2024–31/01/2027
Principle investigator for the project (University for Continuing Education Krems): Markus Gusenbauer
Funding: Bundesländer (inkl. deren Stiftungen und Einrichtungen)
Magnetism at interfaces: from quantum to reality
Duration: 01/11/2022–31/10/2025
Principle investigator for the project (University for Continuing Education Krems): Markus Gusenbauer
Funding: FWF
Towards the digital twin of a permanent magnet: an enhanced description of microstructure and state
Duration: 01/03/2022–28/02/2026
Principle investigator for the project (University for Continuing Education Krems): Markus Gusenbauer
Funding: FWF
Completed projects
The Effect of Interfaces on Magnetisation Reversal in MnAl-C
Duration: 01/10/2017–30/11/2020
Principle investigator for the project (University for Continuing Education Krems): Markus Gusenbauer
Funding: FWF
Program: DACH
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
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
Lectures (Extract Research Database)
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