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000892464 1001_ $$0P:(DE-Juel1)192255$$aBode, Mathis$$b0$$eCorresponding author$$ufzj
000892464 245__ $$aUsing physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows
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000892464 536__ $$0G:(DE-Juel1)jhpc55_20190501$$aUsing deep learning to predict statistics of turbulent flows at high Reynolds numbers (jhpc55_20190501)$$cjhpc55_20190501$$fUsing deep learning to predict statistics of turbulent flows at high Reynolds numbers$$x1
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000892464 7001_ $$00000-0003-0038-5249$$aGauding, Michael$$b1
000892464 7001_ $$00000-0001-6944-015X$$aLian, Zeyu$$b2
000892464 7001_ $$00000-0002-7936-8574$$aDenker, Dominik$$b3
000892464 7001_ $$00000-0002-6213-080X$$aDavidovic, Marco$$b4
000892464 7001_ $$aKleinheinz, Konstantin$$b5
000892464 7001_ $$0P:(DE-Juel1)158080$$aJitsev, Jenia$$b6$$ufzj
000892464 7001_ $$00000-0001-5656-0961$$aPitsch, Heinz$$b7
000892464 773__ $$0PERI:(DE-600)2197968-6$$a10.1016/j.proci.2020.06.022$$gVol. 38, no. 2, p. 2617 - 2625$$n2$$p2617 - 2625$$tProceedings of the Combustion Institute$$v38$$x1540-7489$$y2021
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