Predicting the future with magnons : forecasting chaotic time series with reservoir computing

Abstract

Forecasting complex, chaotic signals is a central challenge across science and technology, with implications ranging from secure communications to climate modeling. Here we demonstrate that magnons, the collective spin excitations in magnetically ordered materials, can serve as an efficient physical reservoir for predicting such dynamics. Using a magnetic vortex-state microdisk as a magnon-scattering reservoir, we show that intrinsic nonlinear interactions transform a simple microwave input into a high-dimensional spectral output suitable for time-series predictions. Trained on the Mackey-Glass benchmark, which generates a cyclic yet aperiodic time series, the system achieves accurate and reliable predictions that rival other state-of-the-art physical reservoirs. We further identify key design principles: spectral resolution governs the tradeoff between dimensionality and accuracy, while combining multiple device geometries systematically improves performance. These results establish magnonics as a promising platform for unconventional computing, offering a path toward scalable and CMOS-compatible hardware for real-time prediction tasks.