Fast inverse estimation of hydraulic conductivity field based on a deep convolutional-cycle generative adversarial neural network

Pan, Zidong; Lu, Wenxi; Wang, Han; Bai, Yukun

doi:10.1016/j.jhydrol.2022.128420

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…A fully-linear DenseNet was designed and trained to identify a leak to a pipe in the suspected leak area. In turn, Pan [27] applied a convolutional network for inverse estimation of hydraulic conductivity field, Guo and Vu [28,29] proposed the applications of convolutional neural networks for solving hydraulic tomography issues.…”

Section: Neural Identificationmentioning

confidence: 99%

Artificial Intelligence Methods in Hydraulic System Design

Filo

2023

Energies

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Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.

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Section: Neural Identificationmentioning

confidence: 99%