The computational cost associated with simulating fluid flows can make it infeasible to run many simulations across multiple flow conditions. Building upon concepts from generative modeling, we introduce a new method for learning neural network models capable of performing efficient parameterized simulations of fluid flows. Evaluated on their ability to simulate both two-dimensional and three-dimensional fluid flows, trained models are shown to capture local and global properties of the flowfields at a wide array of flow conditions. Furthermore, flow simulations generated by the trained models are shown to be orders of magnitude faster than the corresponding computational fluid dynamics simulations.

与模拟流体流相关的计算成本使在多种流动条件下运行许多模拟变得不可行。基于生成建模的概念，我们介绍了一种学习神经网络模型的新方法，该方法能够执行有效的流体流动参数化仿真。对它们模拟二维和三维流体流动的能力进行了评估，并显示了训练有素的模型可以捕获各种流动条件下流场的局部和全局特性。此外，由训练后的模型生成的流动模拟显示比相应的计算流体动力学模拟快几个数量级。