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4 October 2024 Optical forces and torques in the geometrical optics approximation calculated with neural networks
Agnese Callegari, David Bronte Ciriza, Alessandro Magazzù, Gunther D. Barbosa, Antonio A. R. Neves, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò
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Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC1311809 (2024) https://doi.org/10.1117/12.3027690
Event: Nanoscience + Engineering, 2024, San Diego, California, United States
Abstract
Optical tweezers manipulate microscopic objects with light by exchanging momentum and angular momentum between particle and light, generating optical forces and torques. Understanding and predicting them is essential for designing and interpreting experiments. Here, we focus on geometrical optics and optical forces and torques in this regime, and we employ neural networks to calculate them. Using an optically trapped spherical particle as a benchmark, we show that neural networks are faster and more accurate than the calculation with geometrical optics. We demonstrate the effectiveness of our approach in studying the dynamics of systems that are computationally “hard” for traditional computation.
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Agnese Callegari, David Bronte Ciriza, Alessandro Magazzù, Gunther D. Barbosa, Antonio A. R. Neves, Maria A. Iatì, Giovanni Volpe, and Onofrio M. Maragò "Optical forces and torques in the geometrical optics approximation calculated with neural networks", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC1311809 (4 October 2024); https://doi.org/10.1117/12.3027690
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KEYWORDS
Geometrical optics
Neural networks
Particles
Angular momentum
Laser scattering
Light scattering
Optical tweezers
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