STENCIL-NET for equation-free forecasting from data

We present an artificial neural network architecture, termed STENCIL-NET, for equation-free forecasting of spatiotemporal dynamics from data. STENCIL-NET works by learning a discrete propagator that is able to reproduce the spatiotemporal dynamics of the training data. This data-driven propagator ca...

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Detalles Bibliográficos
Autores principales: Maddu, Suryanarayana, Sturm, Dominik, Cheeseman, Bevan L., Müller, Christian L., Sbalzarini, Ivo F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406911/
https://www.ncbi.nlm.nih.gov/pubmed/37550328
http://dx.doi.org/10.1038/s41598-023-39418-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406911/
https://www.ncbi.nlm.nih.gov/pubmed/37550328
http://dx.doi.org/10.1038/s41598-023-39418-6

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