Ing. Dr. Harald Özelt, MSc
- harald.oezelt@donau-uni.ac.at
- +43 2622 23420-56
-
+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
Projects (Extract Research Database)
Running projects
Magnetic Multiscale Modelling Suite
Duration: 01/01/2024–31/12/2027
Principle investigator for the project (University for Continuing Education Krems): Thomas Schrefl
Funding: EU
Data driven magnet design through combinatorial synthesis and micromagnetic graph networks
Duration: 01/03/2023–31/03/2026
Principle investigator for the project (University for Continuing Education Krems): Harald Özelt
Funding: FWF
Design of Nanocomposite Magnets by Machine Learning
Duration: 01/11/2022–30/04/2027
Principle investigator for the project (University for Continuing Education Krems): Harald Özelt
Funding: FWF
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
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
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
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
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
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
Lectures (Extract Research Database)
Active Learning-Assisted Optimization of Magnetic Nanostructures
International Conference on Magnetism, ICM2024, 03/07/2024
Inverse design of Exchange Spring Nanostructures with Convolutional Neural Networks
WPI & MMM workshop on Inverse-design magnonics, 20/02/2024
Machine learning based optimization of hard-/soft magnetic nanostructures
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
Convolutional neural networks to predict properties of magnetic nanostructures
IEEE International Magnetics Conference INTERMAG 2023, 19/05/2023
Multiscaling strategies in computational magnet design
Going Green – CARE INNOVATION 2023, 11/05/2023
Reduced Order Model for Large Multigrain Systems
67th Annual Conference on Magnetism and Magnetic Materials (MMM 2022), 03/11/2022
Machine learning as building block for macromagnetic simulations
CMAM 2022, 31/08/2022
Machine Learning for Relating Structure and Coercivity of Permanent Magnets
Virtual REPM 2021, 09/06/2021
Macromagnetic Simulations by Micromagnetic Superposition
MMM2020 (Magnetism and Magnetic Materials), 06/11/2020