Inferring turbulent environments via machine learning

Buzzicotti, Michele; Bonaccorso, Fabio

doi:10.1140/epje/s10189-022-00258-3

Cited by 6 publications

(2 citation statements)

References 71 publications

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“…In this paper, we perform a systematic quantitative comparison among three data-driven methods (no information on the underlying equations) to reconstruct highly complex two-dimensional (2-D) fields from a typical geophysical set-up, such as that of rotating turbulence. The first two methods are connected with a linear model reduction, the so-called proper orthogonal decomposition (POD) and the third is based on a fully nonlinear convolutional neural network (CNN) embedded in a framework of a generative adversarial network (GAN) (Goodfellow et al 2014;Deng et al 2019;Subramaniam et al 2020;Buzzicotti et al 2021;Guastoni et al 2021;Kim et al 2021;Buzzicotti & Bonaccorso 2022;Yousif et al 2022). Proper orthogonal decomposition is widely used for pattern recognition (Sirovich & Kirby 1987;Fukunaga 2013), optimization (Singh et al 2001) and data assimilation (Romain, Chatellier & David 2014;Suzuki 2014).…”

Section: Introductionmentioning

confidence: 99%

Multi-scale reconstruction of turbulent rotating flows with proper orthogonal decomposition and generative adversarial networks

Li,

Buzzicotti,

Biferale

et al. 2023

J. Fluid Mech.

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Data reconstruction of rotating turbulent snapshots is investigated utilizing data-driven tools. This problem is crucial for numerous geophysical applications and fundamental aspects, given the concurrent effects of direct and inverse energy cascades. Additionally, benchmarking of various reconstruction techniques is essential to assess the trade-off between quantitative supremacy, implementation complexity and explicability. In this study, we use linear and nonlinear tools based on the proper orthogonal decomposition (POD) and generative adversarial network (GAN) for reconstructing rotating turbulence snapshots with spatial damages (inpainting). We focus on accurately reproducing both statistical properties and instantaneous velocity fields. Different gap sizes and gap geometries are investigated in order to assess the importance of coherency and multi-scale properties of the missing information. Surprisingly enough, concerning point-wise reconstruction, the nonlinear GAN does not outperform one of the linear POD techniques. On the other hand, the supremacy of the GAN approach is shown when the statistical multi-scale properties are compared. Similarly, extreme events in the gap region are better predicted when using GAN. The balance between point-wise error and statistical properties is controlled by the adversarial ratio, which determines the relative importance of the generator and the discriminator in the GAN training.

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Section: Introductionmentioning

confidence: 99%

Multi-scale reconstruction of turbulent rotating flows with proper orthogonal decomposition and generative adversarial networks

Li,

Buzzicotti,

Biferale

et al. 2023

J. Fluid Mech.

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“…In complex flows as well, there have been numerous positive outcomes in nearly all testing scenarios, varying from control problems as single and multi-agents navigation in complex environments [7][8][9][10][11][12][13][14][15], to turbulent control and drag reduction [16][17][18][19][20][21], up to data assimilation problems [22][23][24][25][26][27][28][29][30][31][32][33] to cite few of them. However, applications in fluids are still in their infancy, and the majority of cases are either conducted on highly idealized setups or only showing preliminary results on more realistic conditions.…”

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confidence: 99%

Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives

Buzzicotti

2023

EPL

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In recent years the fluid mechanics community has been intensely focused on pursuing solutions to its long-standing open problems by exploiting the new machine learning, (ML), approaches. The exchange between ML and fluid mechanics is bringing important paybacks in both directions. The first is benefiting from new physics-inspired ML methods and a scientific playground to perform quantitative benchmarks, whilst the latter has been open to a large set of new tools inherently well suited to deal with big data, flexible in scope, and capable of revealing unknown correlations. A special case is the problem of modeling missing information of partially observable systems. The aim of this paper is to review some of the ML algorithms that are playing an important role in the current developments in this field, to uncover potential avenues, and to discuss the open challenges for applications to fluid mechanics.

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Turbulence scaling from deep learning diffusion generative models

Whittaker,

Janik,

2024

Journal of Computational Physics

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Inferring turbulent environments via machine learning

Cited by 6 publications

References 71 publications

Multi-scale reconstruction of turbulent rotating flows with proper orthogonal decomposition and generative adversarial networks

Multi-scale reconstruction of turbulent rotating flows with proper orthogonal decomposition and generative adversarial networks

Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives

Turbulence scaling from deep learning diffusion generative models

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