We propose MeshfreeFlowNet, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs. While being computationally efficient, MeshfreeFlowNet accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet allows for: (i) the output to be sampled at all spatio-temporal resolutions, (ii) a set of Partial Differential Equation (PDE) constraints to be imposed, and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal domains owing to its fully convolutional encoder. We empirically study the performance of MeshfreeFlowNet on the task of super-resolution of turbulent flows in the Rayleigh-Benard convection problem. Across a diverse set of evaluation metrics, we show that MeshfreeFlowNet significantly outperforms existing baselines. Furthermore, we provide a large scale implementation of MeshfreeFlowNet and show that it efficiently scales across large clusters, achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of less than 4 minutes.

提出了一种基于深度学习的新型超分辨率框架MeshfreeFlowNet，它可以在低分辨率下生成连续（无网格）的时空解，并可以采样真实的时空分辨率，以及在其完全卷积编码器的支持下对任意大小的时空域进行固定输入的训练，MeshfreeFlowNet在Rayleigh-Benard对流问题中的湍流流动超分辨率任务上表现出众，并且可以高效地扩展到大型集群上，最高可在128个GPU上达到96.80％的扩展效率，训练时间不到4分钟。

MeshfreeFlowNet：一种物理约束的深度连续空时超分辨率框架