Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Michele Buzzicotti"'
Autor:
Hemant Khatri, Stephen M. Griffies, Benjamin A. Storer, Michele Buzzicotti, Hussein Aluie, Maike Sonnewald, Raphael Dussin, Andrew Shao
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 16, Iss 6, Pp n/a-n/a (2024)
Abstract The climatological mean barotropic vorticity budget is analyzed to investigate the relative importance of surface wind stress, topography, planetary vorticity advection, and nonlinear advection in dynamical balances in a global ocean simulat
Externí odkaz:
https://doaj.org/article/dbc793163f4a4675b36fe8f130c51c0f
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 84, Iss 1, Pp 1-25 (2024)
Abstract We present a new supervised deep-learning approach to the problem of the extraction of smeared spectral densities from Euclidean lattice correlators. A distinctive feature of our method is a model-independent training strategy that we implem
Externí odkaz:
https://doaj.org/article/0af50edd10bc4890955ec9a01dbf015d
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-9 (2022)
Ocean spatial scale analysis has struggled to capture the vast dynamic range at planetary scales. Here the authors employ a method to probe circulation patterns in the World Ocean, thus opening a promising new window for measuring and understanding t
Externí odkaz:
https://doaj.org/article/046dc0940aa146ddbfc064b3de5b5c64
Publikováno v:
Atmosphere, Vol 15, Iss 1, p 60 (2023)
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 spa
Externí odkaz:
https://doaj.org/article/d517336f649347fab82c7faeca336ca8
Autor:
Michele Buzzicotti, Akshay Bhatnagar, Luca Biferale, Alessandra S Lanotte, Samriddhi Sankar Ray
Publikováno v:
New Journal of Physics, Vol 18, Iss 11, p 113047 (2016)
We study small-scale and high-frequency turbulent fluctuations in three-dimensional flows under Fourier-mode reduction. The Navier–Stokes equations are evolved on a restricted set of modes, obtained as a projection on a fractal or homogeneous Fouri
Externí odkaz:
https://doaj.org/article/8d6d12f718f5481697bde28e4cd05d08
Our understanding of the ocean’s spatial scales and their coupling has been derived mostly from Fourier analysis in small "representative" regions, typically a few hundred kilometers in size, that cannot capture the vast dynamic range at planetary
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46ff5636c05f5362bc7a0483447c68a4
https://doi.org/10.5194/egusphere-egu23-561
https://doi.org/10.5194/egusphere-egu23-561
Autor:
Hemant Khatri, Stephen M Griffies, Benjamin A Storer, Michele Buzzicotti, Hussein Aluie, Maike Sonnewald, Raphael Dussin, Andrew E. Shao
The climatological mean barotropic vorticity budget is analyzed to investigate the relative importance of surface wind stress, topography and nonlinear advection in dynamical balances in a global ocean simulation. In addition to a pronounced regional
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31341c87de304b79d5c85642e2acb274
https://doi.org/10.22541/essoar.168394747.71837050/v1
https://doi.org/10.22541/essoar.168394747.71837050/v1
Abstract Inference problems for two-dimensional snapshots of rotating turbulent flows are studied. We perform a systematic quantitative benchmark of point-wise and statistical reconstruction capabilities of the linear Extended Proper Orthogonal Decom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b46c4c9601190ec702a8cc327c86008
https://hdl.handle.net/2108/322640
https://hdl.handle.net/2108/322640
We investigate the capabilities of Physics-Informed Neural Networks (PINNs) to reconstruct turbulent Rayleigh-Benard flows using only temperature information. We perform a quantitative analysis of the quality of the reconstructions at various amounts
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57e500ecee4b328fd566a6c5e4949bd4
http://arxiv.org/abs/2301.07769
http://arxiv.org/abs/2301.07769
Autor:
Michele Buzzicotti
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 importan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a1a5362a917787f91984f8365638591f
https://hdl.handle.net/2108/321982
https://hdl.handle.net/2108/321982