The Eulerian fluid simulation is an important HPC application. The neural network has been applied to accelerate it. The current methods that accelerate the fluid simulation with neural networks lack flexibility and generalization. In this paper, we tackle the above limitation and aim to enhance the applicability of neural networks in the Eulerian fluid simulation. We introduce Smartfluidnet, a framework that automates model generation and application. Given an existing neural network as input, Smartfluidnet generates multiple neural networks before the simulation to meet the execution time and simulation quality requirement. During the simulation, Smartfluidnet dynamically switches the neural networks to make the best efforts to reach the user requirement on simulation quality. Evaluating with 20,480 input problems, we show that Smartfluidnet achieves 1.46x and 590x speedup comparing with a state-of-the-art neural network model and the original fluid simulation respectively on an NVIDIA Titan X Pascal GPU, while providing better simulation quality than the state-of-the-art model.

中文翻译：

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基于自适应神经网络的逼近加速欧拉流体模拟

欧拉流体模拟是重要的HPC应用程序。神经网络已被应用来加速它。当前使用神经网络加速流体模拟的方法缺乏灵活性和通用性。在本文中，我们解决了上述限制，旨在增强神经网络在欧拉流体模拟中的适用性。我们介绍Smartfluidnet，该框架可自动执行模型生成和应用程序。给定现有的神经网络作为输入，Smartfluidnet在仿真之前会生成多个神经网络，以满足执行时间和仿真质量要求。在仿真过程中，Smartfluidnet会动态切换神经网络，以尽最大努力满足用户对仿真质量的要求。评估20,480个输入问题，