Single-snapshot machine learning for turbulence super resolution
<|reference_start|>Single-snapshot machine learning for turbulence super resolution: Modern machine-learning techniques are generally considered data-hungry. However, this may not be the case for turbulence as each of its snapshots can hold more information than a single data file in general machine-learning applications. This study asks the question of whether nonlinear machine-learning techniques can effectively extract physical insights even from as little as a single snapshot of a turbulent vortical flow. As an example, we consider machine-learning-based super-resolution analysis that reconstructs a high-resolution field from low-resolution data for two-dimensional decaying turbulence. We reveal that a carefully designed machine-learning model trained with flow tiles sampled from only a single snapshot can reconstruct vortical structures across a range of Reynolds numbers. Successful flow reconstruction indicates that nonlinear machine-learning techniques can leverage scale-invariance properties to learn turbulent flows. We further show that training data of turbulent flows can be cleverly collected from a single snapshot by considering characteristics of rotation and shear tensors. The present findings suggest that embedding prior knowledge in designing a model and collecting data is important for a range of data-driven analyses for turbulent flows. More broadly, this work hopes to stop machine-learning practitioners from being wasteful with turbulent flow data.<|reference_end|>
@article{fukami2024single-snapshot,
    title={Single-snapshot machine learning for turbulence super resolution},
    author={Kai Fukami, Kunihiko Taira},
    journal={arXiv preprint arXiv:2409.04923},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04923},
    primaryClass={physics.flu-dyn cs.LG physics.comp-ph}
}