In the absence of high-resolution samples, super-resolution of sparse observations on dynamical systems is a challenging problem with wide-reaching applications in experimental settings. We showcase the application of physics-informed convolutional neural networks for super-resolution of sparse observations on grids. Results are shown for the chaotic-turbulent Kolmogorov flow, demonstrating the potential of this method for resolving finer scales of turbulence when compared with classic interpolation methods, and thus effectively reconstructing missing physics.

翻译：在没有高分辨率样本的情况下,动态系统观测极少的超分辨率是一个具有挑战性的问题,在实验环境中应用范围很广。我们展示了物理学知情的进化神经网络在电网观测稀少观测的超分辨率的应用。结果显示了混乱的突变科莫戈洛夫流的结果,显示了这种方法与经典的内插方法相比在解决更细的动荡规模方面的潜力,从而有效地重建了缺失的物理学。