论文标题
物理信息的CNN，用于在动态系统上进行稀疏观测的超分辨率
Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems
论文作者
论文摘要
在没有高分辨率样本的情况下，动态系统上稀疏观测的超分辨率是实验环境中广泛应用的挑战性问题。我们展示了物理知识的卷积神经网络在网格上的稀疏观测中的应用。与经典的插值方法相比，该方法显示了混乱的尿瘤kolmogorov流的结果，证明了这种方法可以解决较细的湍流，从而有效地重建缺失的物理学。
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.
