论文标题
在动态系统上，物理知识的卷积神经网络用于腐败
Physics-Informed Convolutional Neural Networks for Corruption Removal on Dynamical Systems
论文作者
论文摘要
对动态系统的测量，无论是实验性还是其他方式，通常都会遭受能够引入腐败的不准确性。删除这是物理科学中基本重要性的问题。在这项工作中，我们提出了物理知识的卷积神经网络，以消除固定的腐败，从而提供了从数据中提取物理解决方案的方法，允许访问在搭配点处的部分基础真相观察。我们展示了混乱的流动流动方式中2D不可压缩的Navier-Stokes方程的方法，这表明了对腐败方式的鲁棒性。
Measurements on dynamical systems, experimental or otherwise, are often subjected to inaccuracies capable of introducing corruption; removal of which is a problem of fundamental importance in the physical sciences. In this work we propose physics-informed convolutional neural networks for stationary corruption removal, providing the means to extract physical solutions from data, given access to partial ground-truth observations at collocation points. We showcase the methodology for 2D incompressible Navier-Stokes equations in the chaotic-turbulent flow regime, demonstrating robustness to modality and magnitude of corruption.
