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Fluid dynamics

Catching up with missing particles

Nature Machine Intelligence volume 6, pages 13–14 (2024)Cite this article

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The implementation of particle-tracking techniques with deep neural networks is a promising way to determine particle motion within complex flow structures. A graph neural network-enhanced method enables accurate particle tracking by significantly reducing the number of lost trajectories.

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Fig. 1: GotFlow3D working scheme.

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Authors and Affiliations

  1. Institut Pprime, CNRS, Université de Poitiers, ISAE-ENSMA, Poitiers, France

    Séverine Atis & Lionel Agostini

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  1. Séverine Atis
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  2. Lionel Agostini
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Correspondence to Séverine Atis.

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The authors declare no competing interests.

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Atis, S., Agostini, L. Catching up with missing particles. Nat Mach Intell 6, 13–14 (2024). https://doi.org/10.1038/s42256-023-00770-x

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