[docenti-chimica] 5o colloquio interdip. sui nuovi metodi computazionali; M. Buzzicotti: "Generation and Reconstruction of Lagrangian Turbulence with,Stochastic Generative Models", in 19, 14:30 del 4/12

[Fabio Peresempio < >]

Ai Proff. Ordinari
Ai Proff. Associati
Ai Ricercatori
________________________

Gentili colleghe/i,

ricevo e vi giro la comunicazione con argomento in oggetto.


Cordialmente,

Prof. Lucio Cerrito
Coordinatore della Macroarea di Scienze MM.FF.NN.

_____________

    Gentili Colleghe e Colleghi,
il terzo colloquio interdipartimentale sui nuovi metodi computazionali si 
svolgerà in
     aula 19, a partire dalle 14:30
     di mercoledì 4 dicembre 2024.

L'oratore sarà 
     Michele Buzzicotti,
     del Dipartimento di Fisica
che ci parlerà di
     "Generation and Reconstruction of Lagrangian
      Turbulence with,Stochastic Generative Models".
Il riassunto del contenuto del seminario è riportato alla fine del presente 
messaggio e nel volantino allegato.


  Con i nostri più cordiali saluti,
Gianfranco Bocchinfuso     (Dip. di Scienze e Tecnologie Chimiche),
Michele Buzzicotti         (Dip. di Fisica),
Dario Del Moro             (Dip. di Fisica),
Ugo Locatelli              (Dip. di Matematica),
Blasco Morozzo Della Rocca (Dip. di Biologia),
Gerardo Pepe               (Dip. di Biologia).



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Author:    Michele Buzzicotti
Dipartimento di Fisica,
Università degli studi di Roma "Tor Vergata"

Title: "Generation and Reconstruction of Lagrangian Turbulence 
with,Stochastic Generative Models"

Abstract.
Lagrangian turbulence lies at the core of numerous applied and fundamental 
problems. However, despite decades of theoretical, numerical, and 
experimental research, no existing model can accurately reproduce particle 
trajectories’ statistical and topological properties in turbulent flows.
This talk presents a machine learning framework based on a state-of-the-art 
diffusion model to generate single particle trajectories in three-dimensional 
turbulence at high Reynolds numbers. This approach bypasses the need for 
direct numerical simulations or experiments to obtain reliable Lagrangian 
data. Our results show that the model reproduces key statistical features 
across time scales, including fat-tailed velocity increment distributions, 
and anomalous scaling laws.
Additionally, we extend this method to reconstruct missing spatial and 
velocity data along trajectories of small objects passively advected by 
turbulent flows, such as oceanic drifters from NOAA’s Global Drifter Program. 
The method accurately reconstructs velocity signals while preserving 
non-Gaussian, intermittent scale-by-scale properties. Notably, the model is 
flexible enough to handle different data gap configurations and to exploit 
correlations enabling superior performance over traditional Gaussian Process 
Regression methods.
This work highlights the potential of machine learning in advancing 
Lagrangian turbulence research and addressing longstanding challenges in the 
field.

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