Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations

Abstract

Deep learning has an increasing impact to assist research. Here, authors show that a dynamical neural network, trained on a minimal amount of data, can predict the behaviour of spintronic devices with high accuracy and an extremely efficient simulation time.