Reconstruction of turbulent data with deep generative models for
semantic inpainting from TURB-Rot database
- URL: http://arxiv.org/abs/2006.09179v2
- Date: Mon, 14 Jun 2021 09:26:53 GMT
- Title: Reconstruction of turbulent data with deep generative models for
semantic inpainting from TURB-Rot database
- Authors: M. Buzzicotti, F. Bonaccorso, P. Clark Di Leoni, L. Biferale
- Abstract summary: We study the applicability of tools developed by the computer vision community for features learning and semantic image inpainting to perform data reconstruction of fluid turbulence configurations.
We investigate the capability of Convolutional Neural Networks embedded in a Deep Generative Adversarial Model (Deep-GAN) to generate missing data in turbulence.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study the applicability of tools developed by the computer vision
community for features learning and semantic image inpainting to perform data
reconstruction of fluid turbulence configurations. The aim is twofold. First,
we explore on a quantitative basis, the capability of Convolutional Neural
Networks embedded in a Deep Generative Adversarial Model (Deep-GAN) to generate
missing data in turbulence, a paradigmatic high dimensional chaotic system. In
particular, we investigate their use in reconstructing two-dimensional damaged
snapshots extracted from a large database of numerical configurations of 3d
turbulence in the presence of rotation, a case with multi-scale random features
where both large-scale organised structures and small-scale highly intermittent
and non-Gaussian fluctuations are present. Second, following a reverse
engineering approach, we aim to rank the input flow properties (features) in
terms of their qualitative and quantitative importance to obtain a better set
of reconstructed fields. We present two approaches both based on Context
Encoders. The first one infers the missing data via a minimization of the L2
pixel-wise reconstruction loss, plus a small adversarial penalisation. The
second searches for the closest encoding of the corrupted flow configuration
from a previously trained generator. Finally, we present a comparison with a
different data assimilation tool, based on Nudging, an equation-informed
unbiased protocol, well known in the numerical weather prediction community.
The TURB-Rot database, http://smart-turb.roma2.infn.it, of roughly 300K 2d
turbulent images is released and details on how to download it are given.
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