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.
Sign up or log in to bookmark your favorites and sync them to your phone or calendar.
We introduce a hybrid HPC–ML framework for efficient modeling of magnon–photon interactions. The HPC component uses an explicit FDTD leap-frog Maxwell–LLG solver (second-order accurate), solving Maxwell’s equations in nonmagnetic regions and adding the LLG equation where ferromagnets are present. Parallelization leverages AMReX’s MPI+X model for multicore CPUs or GPUs, partitioning the domain among MPI ranks. Data collected from nine points in the ferromagnet feed a Long Expressive Memory (LEM) encoder–decoder, trained with a composite loss function (reconstruction, prediction, and physics) and guided by Curriculum Learning. During training, we begin with shorter sequences, no physics enforcement, and a higher learning rate, then move to longer sequences, physics constraints, and a lower rate. Using just 1 ns of high-fidelity simulation data, the ML surrogate accurately predicts the magnetic-field evolution and matches frequency responses (13–18 GHz) under various DC biases. With physics constraints included, errors remain low even for longer sequences. The model reproduces transmission spectra and captures both primary and dual resonances (1800–2200 Oe) with high precision, achieving errors below 2.5% and demonstrating robust spatial and spectral generalization.
