Deep kernel learning of dynamical models from high-dimensional noisy data

This work proposes a stochastic variational deep kernel learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The framework is composed of an encoder that compresses high-dimensional measurements into low-dimensional state variables, and...

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Detalles Bibliográficos
Autores principales: Botteghi, Nicolò, Guo, Mengwu, Brune, Christoph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9747975/
https://www.ncbi.nlm.nih.gov/pubmed/36513711
http://dx.doi.org/10.1038/s41598-022-25362-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9747975/
https://www.ncbi.nlm.nih.gov/pubmed/36513711
http://dx.doi.org/10.1038/s41598-022-25362-4

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