流体流动的参数条件序贯生成模型
Parameter-Conditioned Sequential Generative Modeling of Fluid Flows
摘要：
与模拟流体流动相关联的计算成本可能使得在多个流动条件下运行许多模拟是不可行的。基于生成式建模的概念，我们引入了一种新的方法来学习神经网络模型，能够对流体流动进行有效的参数化模拟。评估其模拟二维和三维流体流动的能力，训练的模型被证明可以在各种流动条件下捕获流场的局部和全局特性。此外，由训练模型生成的流动模拟被示出为比相应的计算流体动力学模拟快几个数量级。
英文摘要：
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 flow fields 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.
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