Our dynamics-based machine learning algorithm for nonlinear reduced models from data has just appeared in Nature Communications.
M. Cenedese, J. Axås, B. Bäuerlein, K. Avila and G. Haller, Data-driven modeling and prediction of non-linearizable dynamics via spectral submanifolds, Nature Communications, 13 (2022) 872. doi: 10.1038/s41467-022-28518-y
Nature Communications also selects our work as a Feature Article in the area of Applied physics and mathematics.
This research is highlighted by this ETH Zurich press release.
Researchers at ETH Zurich have developed a new #algorithm that allows them to model the dynamics of physical systems from observations. In the future it could be applied to the onset of #turbulence and tipping points in #climate. @eth_dmavt https://t.co/huRHxuYDoQ
— ETH Zurich (@ETH_en) February 16, 2022