The Sched app allows you to build your schedule but is not a substitute for your event registration. You must be registered for the event to participate in the sessions. If you have not registered but would like to join us, please go to the event registration page to find out more information.
This schedule is automatically displayed in Central Time (UTC/GMT -6 hours). To see the schedule in your preferred timezone, please select from the drop-down menu to the right, above "Filter by Date."
IMPORTANT NOTE: Timing of sessions and room locations are subject to change.
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To comprehensively investigate multiphysics coupling in spintronic devices, GPU acceleration is essential to address the spatial and temporal disparities inherent in micromagnetic simulations. Beyond traditional numerical methods, machine learning (ML) offers a powerful approach to replace and accelerate computationally expensive routines, particularly in evaluating demagnetization fields. Leveraging AMReX and python-based ML workflows, we developed an open-source micromagnetics tool that integrates ML-driven surrogate models to enhance computational efficiency. By replacing costly demagnetization field calculations with neural network-based approximations, the framework significantly accelerates simulations while maintaining accuracy. In addition to supporting key magnetic interactions—including Zeeman, demagnetization, anisotropy, exchange, and Dzyaloshinskii-Moriya interactions—it is validated on µMAG standard problems, widely accepted DMI benchmarks, and Skyrmion-based applications. This ML-accelerated approach improves computational performance and enables large-scale, data-driven micromagnetics simulations, advancing the study of spintronic and electronic systems.
