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Machine learning reveals complex behaviours in optically trapped particles
Machine Learning: Science and Technology ( IF 6.013 ) Pub Date : 2020-10-08 , DOI: 10.1088/2632-2153/abae76 Isaac C D Lenton 1 , Giovanni Volpe 2 , Alexander B Stilgoe 1 , Timo A Nieminen 1 , Halina Rubinsztein-Dunlop 1
Machine Learning: Science and Technology ( IF 6.013 ) Pub Date : 2020-10-08 , DOI: 10.1088/2632-2153/abae76 Isaac C D Lenton 1 , Giovanni Volpe 2 , Alexander B Stilgoe 1 , Timo A Nieminen 1 , Halina Rubinsztein-Dunlop 1
Affiliation
Since their invention in the 1980s, optical tweezers have found a wide range of applications, from biophotonics and mechanobiology to microscopy and optomechanics. Simulations of the motion of microscopic particles held by optical tweezers are often required to explore complex phenomena and to interpret experimental data. For the sake of computational efficiency, these simulations usually model the optical tweezers as an harmonic potential. However, more physically-accurate optical-scattering models are required to accurately model more onerous systems; this is especially true for optical traps generated with complex fields. Although accurate, these models tend to be prohibitively slow for problems with more than one or two degrees of freedom (DoF), which has limited their broad adoption. Here, we demonstrate that machine learning permits one to combine the speed of the harmonic model with the accuracy of optical-scattering models. Specifically, we show that a neural network can ...
中文翻译:
机器学习揭示了光学捕获粒子中的复杂行为
自从1980年代发明光镊以来,光镊已经得到了广泛的应用,从生物光子学和机械生物学到显微镜和光力学。通常需要对光镊保持的微观粒子运动进行模拟,以探索复杂的现象并解释实验数据。为了提高计算效率,这些模拟通常将光镊建模为谐波电位。但是,需要更多物理上精确的光散射模型才能对较繁琐的系统进行准确建模。对于由复杂场产生的光阱尤其如此。尽管这些模型是准确的,但对于具有一两个以上自由度(DoF)的问题,这些模型的速度往往会过慢,这限制了它们的广泛采用。这里,我们证明了机器学习允许将谐波模型的速度与光散射模型的精度结合起来。具体来说,我们证明了神经网络可以...
更新日期:2020-10-13
中文翻译:
机器学习揭示了光学捕获粒子中的复杂行为
自从1980年代发明光镊以来,光镊已经得到了广泛的应用,从生物光子学和机械生物学到显微镜和光力学。通常需要对光镊保持的微观粒子运动进行模拟,以探索复杂的现象并解释实验数据。为了提高计算效率,这些模拟通常将光镊建模为谐波电位。但是,需要更多物理上精确的光散射模型才能对较繁琐的系统进行准确建模。对于由复杂场产生的光阱尤其如此。尽管这些模型是准确的,但对于具有一两个以上自由度(DoF)的问题,这些模型的速度往往会过慢,这限制了它们的广泛采用。这里,我们证明了机器学习允许将谐波模型的速度与光散射模型的精度结合起来。具体来说,我们证明了神经网络可以...