• Corpus ID: 244920615

Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models

@article{Momenifar2021EmulatingSR,
  title={Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models},
  author={Mohammadreza Momenifar and Enmao Diao and Vahid Tarokh and Andrew D. Bragg},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.03469}
}
We use a data-driven approach to model a three-dimensional turbulent flow using cutting-edge Deep Learning techniques. The deep learning framework incorporates physical constraints on the flow, such as preserving incompressibility and global statistical invariants of velocity gradient tensor. The accuracy of the model is assessed using statistical and physics-based metrics. The data set comes from Direct Numerical Simulation of an incompressible, statistically stationary, isotropic turbulent… 

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