Accelerating optical tweezers simulations with auto-encoders

Isaac C. D. Lenton; Halina Rubinsztein-Dunlop; Scott R. Waitukaitis

doi:10.1117/12.2672782

28 September 2023 Accelerating optical tweezers simulations with auto-encoders

Isaac C. D. Lenton, Halina Rubinsztein-Dunlop, Scott R. Waitukaitis

Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC126550A (2023) https://doi.org/10.1117/12.2672782
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

Machine learning has shown great promise for modelling and analysing optical tweezers experiments. Models have been developed for particle tracking, estimating optical potentials and speeding up optical tweezers simulations. These models push the limits of what traditional techniques can achieve, and have the potential to reduce the cost and improve accessibility of accurate numerical simulations. In this talk, I will provide a brief overview of the current state of machine learning for optical tweezers simulation, current challenges, and potential solutions. In particular, I will focus on auto-encoder networks as a way to improve accuracy and reduce the required amount of training data.

Conference Presentation

Citation Download Citation

Isaac C. D. Lenton, Halina Rubinsztein-Dunlop, and Scott R. Waitukaitis "Accelerating optical tweezers simulations with auto-encoders", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC126550A (28 September 2023); https://doi.org/10.1117/12.2672782

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