Forecasting the outcome of spintronic experiments with neural ordinary differential equations (Conference Presentation)
Damien Querlioz · 2022
This work introduces a methodology for modeling spintronic systems based on a deep learning approach. We show that using a limited amount of micromagnetic simulations or experimental data, we can train a specific type of neural network known as “Neural Ordinary Differential Equations” to predict the behavior of a spintronic system in new situations. On the simulation of a skyrmion-based system, our technique gives equivalent results to micromagnetic simulations but 200 times faster. We also show that based on five milliseconds of experimental data, our method predicted the results of weeks of measurements of spin-torque nanooscillators.