论文标题
在2D层流上进行深度学习模型的基准测试
Benchmarking of Deep Learning models on 2D Laminar Flow behind Cylinder
论文作者
论文摘要
流体力学的快速发展领域最近采用了深度学习来解决该领域的各种问题。本着同样的精神，我们尝试执行直接的数值模拟（DNS），这是计算流体动力学中的任务之一，使用深度学习领域的三个基本体系结构，这些架构都用于解决各种高维问题。我们以自动编码器方式训练这三个模型，为此，数据集被视为将模型作为输入的顺序框架对待。我们观察到，最近引入的称为Transformer的体系结构在选定的数据集上大大优于其对应物。Furthermore，我们得出结论，在CFD领域使用Transformers进行DNS是一个值得探索的有趣的研究领域。
The rapidly advancing field of Fluid Mechanics has recently employed Deep Learning to solve various problems within that field. In that same spirit we try to perform Direct Numerical Simulation(DNS) which is one of the tasks in Computational Fluid Dynamics, using three fundamental architectures in the field of Deep Learning that were each used to solve various high dimensional problems. We train these three models in an autoencoder manner, for this the dataset is treated like sequential frames given to the model as input. We observe that recently introduced architecture called Transformer significantly outperforms its counterparts on the selected dataset.Furthermore, we conclude that using Transformers for doing DNS in the field of CFD is an interesting research area worth exploring.
