A numerical study of fish adaption behaviors in complex environments with a deep reinforcement learning and immersed boundary–lattice Boltzmann method

Fish adaption behaviors in complex environments are of great importance in improving the performance of underwater vehicles. This work presents a numerical study of the adaption behaviors of self-propelled fish in complex environments by developing a numerical framework of deep learning and immersed...

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Detalles Bibliográficos
Autores principales: Zhu, Yi, Tian, Fang-Bao, Young, John, Liao, James C., Lai, Joseph C. S.
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/PMC7814145/
https://www.ncbi.nlm.nih.gov/pubmed/33462281
http://dx.doi.org/10.1038/s41598-021-81124-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814145/
https://www.ncbi.nlm.nih.gov/pubmed/33462281
http://dx.doi.org/10.1038/s41598-021-81124-8

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