Sammendrag
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.
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.