Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems

Key Points

• Magnetization dynamics exhibit complex behavior in driven systems, impacting technological applications.
• Machine learning algorithms demonstrate significant predictive accuracy across metallic spin configurations.
• Observational analysis focused on quasi-equilibrium and driven spin systems under varying conditions.
• Highlights the need for advanced modeling techniques to understand intricate magnetic interactions.