Illuminating the complex behaviour of particles in optical traps with machine learning

Computationally accurate methods for simulating optical tweezers tend to be prohibitively slow, limiting their use to only very simple problems. Simplified models, such as the harmonic model, enable larger simulations by trading off accuracy for speed. In this presentation, I will demonstrate how training an artificial neural network to predict optical force combines the speed of a harmonic model with the accuracy of a semi-analytical method. Artificial neural networks not only enable more extensive and accurate dynamics simulations, but also collaboration through sharing of pre-trained models which can easily be distributed and used on mobile devices and in web browsers, as I will demonstrate.