Convolutional autoencoder and conditional random fields hybrid for predicting spatial-temporal chaos-Reference-Cited by-同舟云学术

Convolutional autoencoder and conditional random fields hybrid for predicting spatial-temporal chaos

Published:2019-12 Issue:12 Volume:29 Page:123116
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Herzog S.¹²³,Wörgötter F.²^ORCID,Parlitz U.¹³⁴^ORCID

Affiliation:

1. Max Planck Institute for Dynamics and Self-Organization, Am Fassberg 17, 37077 Göttingen, Germany

2. Third Institute of Physics and Bernstein Center for Computational Neuroscience, University of Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany

3. DZHK (German Centre for Cardiovascular Research), partner site Göttingen, Robert-Koch-Str. 42a, 37075 Göttingen, Germany

4. Institute for the Dynamics of Complex Systems, University of Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany

Funder

European Commission's H2020-Framework

International Max Planck School of Physics of Biology and Complex Systems

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5124926

Reference25 articles.

1. Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events

2. Exponential Time Differencing for Stiff Systems

3. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit

4. Data-Driven Modeling and Prediction of Complex Spatio-Temporal Dynamics in Excitable Media

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