论文标题
Meshfreeflownet：一个物理限制的深度时空超分辨率框架
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
论文作者
论文摘要
我们提出了Meshfreeflownet，这是一个新型的基于深度学习的超分辨率框架，以从低分辨率输入中生成连续（无网格）时空解决方案。在计算高效的同时，网格弗里弗洛内特（Meshfreeflownet）准确地恢复了优质量的关注量。 Meshfreeflownet允许：（i）在所有时空分辨率上进行采样的输出，（ii）要强加的一组部分微分方程（PDE）约束，以及（iii）对固定尺寸输入的固定尺寸输入的培训，该输入是在任意尺寸的时空范围内，该域源于其全兰渡过的脉动。我们从经验上研究了Meshfreeflownet在雷利 - 贝纳德对流问题中湍流超级分辨率的任务。在各种评估指标中，我们表明Meshfreeflownet明显胜过现有的基准。此外，我们提供了MeshFreeFlownet的大规模实施，并表明它有效地缩放了大型群集，达到了96.80％的缩放效率，最多可达128 GPU，训练时间少于4分钟。
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.
