Effectively detecting anomalous diffusion via deep learning

被引：0
|
作者：
Pacheco-Pozo, Adrian [1 ,2 ]
Krapf, Diego [1 ,2 ]
机构：
[1] Colorado State Univ, Dept Elect & Comp Engn, Ft Collins, CO 80523 USA
[2] Colorado State Univ, Sch Biomed Engn, Ft Collins, CO 80523 USA
来源：
NATURE COMPUTATIONAL SCIENCE | 2024年 / 4卷 / 10期
关键词：
10;
D O I：
10.1038/s43588-024-00705-5
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
A deep learning algorithm is presented to classify single-particle tracking trajectories into theoretical models of anomalous diffusion and detect if the trajectory is related to a model not originally found within the training dataset.
引用
收藏
页码：731 / 732
页数：2
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