MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
Event Type
Paper
Applications
Machine Learning, Deep Learning and Artificial Intelligence
Parallel Programming Languages, Libraries, and Models
Best Student Paper Finalist
TP
TimeTuesday, 17 November 202011am - 11:30am EDT
LocationTrack 2
DescriptionWe propose MeshfreeFlowNet, a novel deep learning framework, to generate continuous (grid-free) spatiotemporal 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 spatiotemporal domains owing to its fully convolutional encoder.
We empirically study the performance of PCSR on the task of super-resolution of turbulent flows in the Rayleigh–Bénard convection problem. Across a diverse set of evaluation metrics, we show that PCSR significantly outperforms existing baselines. Furthermore, we provide a large scale implementation of PCSR 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.
We empirically study the performance of PCSR on the task of super-resolution of turbulent flows in the Rayleigh–Bénard convection problem. Across a diverse set of evaluation metrics, we show that PCSR significantly outperforms existing baselines. Furthermore, we provide a large scale implementation of PCSR 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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