Faster and more accurate geometrical-optics optical force calculation using neural networks

TL;DR

This paper introduces neural networks to improve the speed and accuracy of geometrical-optics calculations of optical forces, enabling faster simulations and more complex particle dynamics analysis.

Contribution

It presents a novel neural network approach that surpasses traditional ray discretization methods in optical force computation.

Findings

Neural networks provide faster force calculations.

Enhanced accuracy over traditional methods.

Enabled complex particle dynamics studies.

Abstract

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.