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Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning

By Kevin Carlberg and others
Data I/O poses a significant bottleneck in large-scale CFD simulations; thus, practitioners would like to significantly reduce the number of times the solution is saved to disk, yet retain the ability to recover any field quantity (at any time instance) a posteriori. The objective of this work is therefore to... Show more
December 5, 2018
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Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning
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