GANs enabled super-resolution reconstruction of wind field. (October 2020)

• Record Type: Journal Article
• Authors: Tran, Duy Tan; Robinson, Haakon; Rasheed, Adil; San, Omer; Tabib, Mandar; Kvamsdal, Trond
• Is Part Of: Journal of physics. Volume 1669(2020)
• Publication Date: 2020-10
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1669/1/012029
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: 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.