论文标题
2D中动荡流的超级分辨率：稳定的物理知情神经网络
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks
论文作者
论文摘要
我们提出了一种新设计的神经网络设计，用于解决湍流的零射击超级分辨率问题。我们将Luenberger型观察者嵌入到网络的体系结构中，以告知网络该过程的物理学，并提供误差校正和稳定机制。此外，为了弥补由于存在未知稳定的强迫而导致观察者的性能降低，该网络旨在估算在训练过程中隐含从数据中隐含的不明强迫的贡献。通过运行一组数值实验，我们证明了所提出的网络确实从数据中恢复了未知的强迫，并且能够从低分辨率噪声观测值中预测高分辨率中的湍流。
We propose a new design of a neural network for solving a zero shot super resolution problem for turbulent flows. We embed Luenberger-type observer into the network's architecture to inform the network of the physics of the process, and to provide error correction and stabilization mechanisms. In addition, to compensate for decrease of observer's performance due to the presence of unknown destabilizing forcing, the network is designed to estimate the contribution of the unknown forcing implicitly from the data over the course of training. By running a set of numerical experiments, we demonstrate that the proposed network does recover unknown forcing from data and is capable of predicting turbulent flows in high resolution from low resolution noisy observations.
