Kernel methods for center manifold approximation and a weak data-based version of the center manifold theorem

DOI10.1016/j.physd.2021.133007OpenAlexW3106954971MaRDI QIDQ2077601

Dominik Wittwar, Boumediene Hamzi, Bernard Haasdonk, Gabriele Santin

Publication date: 21 February 2022

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

Full work available at URL: https://arxiv.org/abs/2012.00338

zbMATH Keywords

kernel methodscenter manifolddata-based modeling

Mathematics Subject Classification ID

Ordinary differential equations (34-XX) Partial differential equations (35-XX)

Related Items (4)

Learning dynamical systems from data: a simple cross-validation perspective. III: Irregularly-sampled time series ⋮ Learning dynamical systems from data: a simple cross-validation perspective. IV: Case with partial observations ⋮ A note on microlocal kernel design for some slow-fast stochastic differential equations with critical transitions and application to EEG signals ⋮ One-shot learning of stochastic differential equations with data adapted kernels

Uses Software

• GaussQR

Cites Work

• Unnamed Item
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• On Learning Vector-Valued Functions
• Theory of Reproducing Kernels
• Scattered Data Approximation

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