A note on microlocal kernel design for some slow-fast stochastic differential equations with critical transitions and application to EEG signals

DOI10.1016/J.PHYSA.2023.128583OpenAlexW4321438544MaRDI QIDQ2700697

Boumediene Hamzi, Léo Paillet, Houman Owhadi

Publication date: 27 April 2023

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2023.128583

zbMATH Keywords

critical transitionsdata-based kernelskernel mode decomposition (KMD)learning noise from datalearning signal from datamicro-local kernel designslow-fast stochastic differential equations

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Related Items (1)

Learning dynamical systems from data: a simple cross-validation perspective. IV: Case with partial observations

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