Optical forces are often calculated by using geometrical optics to compute the exchange of momentum between particle and light beam. In geometrical optics, the light beam is represented by a certain number of rays. This sets a trade-off between calculation speed and accuracy. Here, we show that using neural networks allows overcoming this limitation, obtaining not only faster but also more accurate simulations. Then, we exploit our neural networks method to study the dynamics of ellipsoidal particles in a double trap, a system that would be computationally impossible otherwise.
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