Predicting the future with magnons
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910193241030656 |
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| author | Xiong, Zeling Heins, Christopher Devolder, Thibaut Kammerbauer, Fabian Kläui, Mathias Fassbender, Jürgen Schultheiss, Helmut Schultheiss, Katrin |
| author_facet | Xiong, Zeling Heins, Christopher Devolder, Thibaut Kammerbauer, Fabian Kläui, Mathias Fassbender, Jürgen Schultheiss, Helmut Schultheiss, Katrin |
| contents | 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 microdisk in the vortex state as a magnon-scattering reservoir, we show that intrinsic nonlinear interactions transform a simple microwave input into a high-dimensional spectral output suitable for reservoir computing, in particular, for time series predictions. Trained on the Mackey-Glass benchmark, which generates a cyclic yet aperiodic time series widely used to test machine-learning models, the system achieves accurate and reliable predictions that rival state-of-the-art physical reservoirs. We further identify key design principles: spectral resolution governs the trade-off 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06382 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting the future with magnons Xiong, Zeling Heins, Christopher Devolder, Thibaut Kammerbauer, Fabian Kläui, Mathias Fassbender, Jürgen Schultheiss, Helmut Schultheiss, Katrin Mesoscale and Nanoscale Physics 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 microdisk in the vortex state as a magnon-scattering reservoir, we show that intrinsic nonlinear interactions transform a simple microwave input into a high-dimensional spectral output suitable for reservoir computing, in particular, for time series predictions. Trained on the Mackey-Glass benchmark, which generates a cyclic yet aperiodic time series widely used to test machine-learning models, the system achieves accurate and reliable predictions that rival state-of-the-art physical reservoirs. We further identify key design principles: spectral resolution governs the trade-off 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. |
| title | Predicting the future with magnons |
| topic | Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2510.06382 |