Multi-fidelity Generative Deep Learning Turbulent Flows [FoDS][ArXiv]
Nicholas Geneva, Nicholas Zabaras
A novel multi-fidelity deep generative model is introduced for the surrogate modeling of high-fidelity turbulent flow fields given the solution of a computationally inexpensive but inaccurate low-fidelity solver.
- Python 3.6.5
- PyTorch 1.6.0
- Matplotlib 3.1.1
- SciPy 1.5.2
- Dataclasses 0.7.0
See requirements.txt for full dependency list.
Find this useful or like this work? Cite us with:
@article{geneva2020multi,
title = "Multi-fidelity generative deep learning turbulent flows",
author = "Nicholas Geneva and Nicholas Zabaras",
journal = "Foundations of Data Science",
volume = "2",
pages = "391",
year = "2020",
issn = "A0000-0002",
doi = "10.3934/fods.2020019",
url = "https://aimsciences.org/article/id/3a9f3d14-3421-4947-a45f-a9cc74edd097"
}