Machine learning to enhance the calculation of optical forces in the geometrical optics approximation
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò
Submitted to: ELS-XIX (2021)
Date: 14 July
Time: 12:50 CEST
We show how machine learning can improve the speed and accuracy of the optical force calculations in the geometrical optics approximation
Since the pioneering work by Ashkin in the 1970’s, optical forces have played a fundamental role in fields like biology, nanotechnology, or atomic physics. In all these fields, numerical simulations are of great help for validating theories, for the planning of experiments, and in the interpretation of the results. However, the calculation of the forces is computationally expensive and prohibitively slow for numerical simulations when the forces need to be calculated many times in a sequential way.
Recently, machine learning has been demonstrated to be a promising approach to improve the speed of these calculations and therefore, to expand the applicability of numerical simulations for experimental design and analysis. In this work we show that machine learning can be used to improve not only the speed but also the accuracy of the force calculation in the geometrical optics regime, valid when the particles are significantly bigger than the wavelength of the incident light. This is first demonstrated for the case of a spherical particle with 3 degrees of freedom and later expanded to 9 degrees of freedom by including all the relevant parameters involved in the optical forces calculation. Machine learning is proved as a compact, accurate, and fast approach for optical forces calculation and presents a tool that can be used to study systems that, due to computation limitations, were out of the scope of the traditional ray optics approach.