Synthetic Lagrangian turbulence by generative diffusion models

Tao Li; Luca Biferale; Fabio Bonaccorso; Martino Andrea Scarpolini; Michele Buzzicotti

doi:10.1038/s42256-024-00810-0

ScienceGate Book Chapters

JOURNAL ARTICLE

Synthetic Lagrangian turbulence by generative diffusion models

Tao Li Luca Biferale Fabio Bonaccorso Martino Andrea Scarpolini Michele Buzzicotti

Year: 2024 Journal: Nature Machine Intelligence Vol: 6 (4)Pages: 393-403 Publisher: Nature Portfolio

DOI: 10.1038/s42256-024-00810-0

Get Full-Text PDF Get Analytical Report

Keywords:

Turbulence Lagrangian Diffusion Generative grammar Statistical physics Mechanics Computer science Physics Applied mathematics Mathematics Artificial intelligence Thermodynamics

Metrics

Cited By

28.11

FWCI (Field Weighted Citation Impact)

Refs

1.00

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Fluid Dynamics and Turbulent Flows

Physical Sciences → Engineering → Computational Mechanics

Meteorological Phenomena and Simulations

Physical Sciences → Earth and Planetary Sciences → Atmospheric Science

Lattice Boltzmann Simulation Studies

Physical Sciences → Engineering → Computational Mechanics

Synthetic Lagrangian turbulence by generative diffusion models

Metrics

Citation History

Topics

Related Documents

CoNFiLD-inlet: Synthetic turbulence inflow using generative latent diffusion models with neural fields

Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence

Turbulence scaling from deep learning diffusion generative models

Synthetic Eulerian Fields and Lagrangian Turbulence

Deterministic diffusion models for Lagrangian turbulence: Robustness and encoding of extreme events