论文标题
使用神经网络更快，更准确的几何镜头光学力量计算
Faster and more accurate geometrical-optics optical force calculation using neural networks
论文作者
论文摘要
通常通过将捕获光束离散到一组射线中并使用几何光学器件来计算动量交换来计算光学力。但是，射线的数量设定了计算速度和准确性之间的权衡。在这里，我们表明，使用神经网络允许一个人克服这一限制，不仅获得更快的模拟，而且获得了更准确的模拟。我们使用光学捕获的球形粒子为此证明了这一点，我们为其获得一个分析解决方案，以用作地面真理。然后，我们利用神经网络提供的加速度来研究双重陷阱中椭圆形粒子的动力学，否则在计算上是不可能的。
Optical forces are often calculated by discretizing the trapping light beam into a set of rays and using geometrical optics to compute the exchange of momentum. However, the number of rays sets a trade-off between calculation speed and accuracy. Here, we show that using neural networks permits one to overcome this limitation, obtaining not only faster but also more accurate simulations. We demonstrate this using an optically trapped spherical particle for which we obtain an analytical solution to use as ground truth. Then, we take advantage of the acceleration provided by neural networks to study the dynamics of an ellipsoidal particle in a double trap, which would be computationally impossible otherwise.
