MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
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.
Cite
@article{arxiv.2005.01463,
title = {MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework},
author = {Chiyu Max Jiang and Soheil Esmaeilzadeh and Kamyar Azizzadenesheli and Karthik Kashinath and Mustafa Mustafa and Hamdi A. Tchelepi and Philip Marcus and Prabhat and Anima Anandkumar},
journal= {arXiv preprint arXiv:2005.01463},
year = {2020}
}
Comments
Supplementary Video: https://youtu.be/mjqwPch9gDo. Accepted to SC20