论文标题
探索物理潜在空间以进行高分辨率流恢复
Exploring Physical Latent Spaces for High-Resolution Flow Restoration
论文作者
论文摘要
我们使用模拟的自由度作为神经网络的潜在空间来探索训练深层神经网络模型与物理模拟的结合。与以前的工作相反，本文将模拟空间的自由度视为神经网络使用的工具。我们证明了这一概念用于学习减少表示形式，因为忠实地在长期范围内忠实地保留正确的解决方案是极具挑战性的，并具有传统的减少表示形式，尤其是对于具有大量小规模特征的解决方案。这项工作重点是使用这种物理，减少的潜在空间来恢复精细模拟，通过训练模型可以根据需要修改减少的物理状态的内容，以最大程度地满足学习目标。这种自治使神经网络能够发现替代动态，从而显着改善了给定任务中的性能。我们证明了从不同的湍流场景到烟羽的各种流体流动的概念。
We explore training deep neural network models in conjunction with physics simulations via partial differential equations (PDEs), using the simulated degrees of freedom as latent space for a neural network. In contrast to previous work, this paper treats the degrees of freedom of the simulated space purely as tools to be used by the neural network. We demonstrate this concept for learning reduced representations, as it is extremely challenging to faithfully preserve correct solutions over long time-spans with traditional reduced representations, particularly for solutions with large amounts of small scale features. This work focuses on the use of such physical, reduced latent space for the restoration of fine simulations, by training models that can modify the content of the reduced physical states as much as needed to best satisfy the learning objective. This autonomy allows the neural networks to discover alternate dynamics that significantly improve the performance in the given tasks. We demonstrate this concept for various fluid flows ranging from different turbulence scenarios to rising smoke plumes.
