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arXiv Machine Learning By Arth Sojitra, Muhammad Abid, Omer San

Extreme-Scale Super-Resolution of Homogeneous Turbulence from a Single Realization

Read the original on arXiv Machine Learning →

The study demonstrates that a wide‑activation deep super‑resolution (WDSR) network can learn to reconstruct unseen two‑dimensional homogeneous isotropic turbulence fields from overlapping patches of a single high‑resolution realization. By training exclusively on these patches, the model achieves extreme super‑resolution factors of x32 and x64, accurately recovering coherent vortical structures and fine‑scale features better than conventional interpolation. Spectral and statistical analyses confirm improved high‑wavenumber energy recovery and multiscale flow characteristics, and comparisons with models trained on multiple full snapshots highlight the advantage of exploiting spatial redundancy within a single realization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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