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MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
Event Type
Paper
Tags
Applications
Machine Learning, Deep Learning and Artificial Intelligence
Parallel Programming Languages, Libraries, and Models
Award Finalists
Best Student Paper Finalist
Registration Categories
TP
TimeTuesday, 17 November 202011am - 11:30am EST
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.
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