Markus Gusenbauer

Dipl.-Ing.(FH) Dr. Markus Gusenbauer is a researcher at the University for Continuing Education Krems, affiliated with the Center for Modelling and Simulation. His work focuses on computational materials science, micromagnetics, and machine learning applications in magnetic materials. He leads projects such as "Towards the digital twin of a permanent magnet" and "Magnetism at interfaces: from quantum to reality", funded by organizations like FWF and Bundesländer. His research includes optimizing permanent magnet performance, analyzing nanoscale defects, and developing machine learning models for material design.

Key research areas include:

• Coercivity enhancement in magnets
• Micromagnetic characterization of MnAl-C materials
• Deep learning for hysteresis property prediction
• Stochastic micromagnetism and mesh-independent simulations

His recent publications (2020-2024) address topics like rare-earth reduction in magnets, twin boundary effects, and conditional physics-informed neural networks. He actively participates in international conferences, presenting work on topics such as machine learning-assisted interface analysis and magnet design strategies.