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Jülich Supercomputing Center; JSC
Using deep learning to predict statistics of turbulent flows at high Reynolds numbers
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Preprint
Jülich Supercomputing Center; JSC
Using deep learning to predict statistics of turbulent flows at high Reynolds numbers
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Preprint
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows
Bode, Mathis
2019
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Bode, Mathis
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Data-Intensive Science and Federated Computing
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Direct Numerical Simulations of Fluid Turbulence at High Reynolds Numbers
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Using deep learning to predict statistics of turbulent flows at high Reynolds numbers
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Jülich Supercomputing Center; JSC
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2019
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