Multi-Scale Reconstruction of Turbulent Rotating Flows with Generative Diffusion Models.
In: Atmosphere, Jg. 15 (2024), Heft 1, S. 60-82
Online
academicJournal
Zugriff:
We address the problem of data augmentation in a rotating turbulence set-up, a paradigmatic challenge in geophysical applications. The goal is to reconstruct information in two-dimensional (2D) cuts of the three-dimensional flow fields, imagining spatial gaps present within each 2D observed slice. We evaluate the effectiveness of different data-driven tools, based on diffusion models (DMs), a state-of-the-art generative machine learning protocol, and generative adversarial networks (GANs), previously considered as the best-performing method both in terms of point-wise reconstruction and the statistical properties of the inferred velocity fields. We focus on two different DMs recently proposed in the specialized literature: (i) RePaint, based on a heuristic strategy to guide an unconditional DM for flow generation by using partial measurements data, and (ii) Palette, a conditional DM trained for the reconstruction task with paired measured and missing data. Systematic comparison shows that (i) DMs outperform the GAN in terms of the mean squared error and/or the statistical accuracy; (ii) Palette DM emerges as the most promising tool in terms of both point-wise and statistical metrics. An important property of DMs is their capacity for probabilistic reconstructions, providing a range of predictions based on the same measurements, enabling uncertainty quantification and risk assessment. [ABSTRACT FROM AUTHOR]
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Multi-Scale Reconstruction of Turbulent Rotating Flows with Generative Diffusion Models.
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Autor/in / Beteiligte Person: | Li, Tianyi ; Lanotte, Alessandra S. ; Buzzicotti, Michele ; Bonaccorso, Fabio ; Biferale, Luca |
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Zeitschrift: | Atmosphere, Jg. 15 (2024), Heft 1, S. 60-82 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 2073-4433 (print) |
DOI: | 10.3390/atmos15010060 |
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