STENCIL-NET for equation-free forecasting from data

STENCIL-NET for equation-free forecasting from data

Abstract 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 prop...

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Bibliographic Details
Main Authors: Suryanarayana Maddu, Dominik Sturm, Bevan L. Cheeseman, Christian L. Müller, Ivo F. Sbalzarini
Format: Article
Language:English
Published: Nature Portfolio 2023-08-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-39418-6

Internet

https://doi.org/10.1038/s41598-023-39418-6

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