Connectivity-informed drainage network generation using deep convolution generative adversarial networks

Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation. In this study, Deep Convolutional Generative Adversarial Networks (DCGANs) were applied to quickly reproduce drainage networks from the alread...

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Detalles Bibliográficos
Autores principales: Kim, Sung Eun, Seo, Yongwon, Hwang, Junshik, Yoon, Hongkyu, Lee, Jonghyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7810735/
https://www.ncbi.nlm.nih.gov/pubmed/33452322
http://dx.doi.org/10.1038/s41598-020-80300-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7810735/
https://www.ncbi.nlm.nih.gov/pubmed/33452322
http://dx.doi.org/10.1038/s41598-020-80300-6

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