MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

• URL: http://arxiv.org/abs/2005.01463v2
• Date: Fri, 21 Aug 2020 04:08:23 GMT
• Title: MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
• Authors: Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar
• Abstract summary: MeshfreeFlowNet is a framework to generate continuous (grid-free)-80% solutions from the low-resolution inputs. MeshfreeFlowNet allows for (i) output to be sampled at all resolutions, (ii) training on fixed-size inputs on arbitrarily sized-temporal domains. We propose a large scale implementation of MeshfreeFlowNet and show that it efficiently scales across large clusters.
• Score: 58.49761896587656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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