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Fluid dynamics

Catching up with missing particles

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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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).

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