Work

Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning

Year: 2021

Type: article

Source: Nature Machine Intelligence

DOI: 10.1038/s42256-021-00402-2

Authors: Kai Fukami, Romit Maulik, Nesar Ramachandra, Koji Fukagata, Kunihiko Taira

Institutions: University of California, Los Angeles, Keio University, Argonne National Laboratory, Argonne Leadership Computing Facility

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, Office of Science, Japan Society for the Promotion of Science, Advanced Scientific Computing Research, Air Force Office of Scientific Research, Army Research Office, Argonne National Laboratory

Awards: G102163711, G107546177, G1313983767, G1664757510, G1692787531, G2287611682, G262962599, G3075337988, Creation and implementation of an innovative flow control paradigm utilizing machine learning, G498139845, G5372758913, G5398989114, G6918803902, Construction of feature extraction method for turbulence big data by machine learning, G741291543, G7428659514, G878601127, G993149817

OpenAlex ID: W3119943312

FWCI: 11.33

Cites: 42

Cited by: 284

Related to: 8