Machine learning to improve the calculation of optical forces in the geometrical optics approximation

Even though in most cases optical forces can be calculated semi-analytically, the computation becomes prohibitively slow in problems where the calculation needs to be repeated several times. Starting from a spherical particle in an optical trap, we show how machine learning can be used to improve not only the speed but also the accuracy of the optical force calculations in the geometrical optics approach. This is demonstrated to work efficiently at least up to 9 degrees of freedom, constituting a tool for exploring problems that were out of the scope of the traditional geometrical optics calculation.