Open AccessGANs enabled super-resolution reconstruction of wind field
Author(s) -
Duy Tan Tran,
Haakon Robinson,
Adil Rasheed,
Omer San,
Mandar Tabib,
Trond Kvamsdal
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1669/1/012029
Subject(s) - terrain , interpolation (computer graphics) , bicubic interpolation , computer science , field (mathematics) , variety (cybernetics) , scale (ratio) , turbulence , resolution (logic) , aerospace engineering , algorithm , meteorology , artificial intelligence , mathematics , engineering , geography , image (mathematics) , pattern recognition (psychology) , cartography , linear interpolation , pure mathematics
Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this paper, we demonstrate a novel approach to address this issue through a combination of fast coarse scale physics based simulator and a family of advanced machine learning algorithm called the Generative Adversarial Networks. The physics-based simulator generates a coarse wind field in a real wind farm and then ESRGANs enhance the result to a much finer resolution. The method outperforms state of the art bicubic interpolation methods commonly utilized for this purpose.