Computing coherent light scattering on the millimetre scale using a recurrent neural network without training

Laurynas Valantinas, Tom Vettenburg (Lead / Corresponding author)

Research output: Working paper/PreprintPreprint

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Abstract

Heterogeneous materials such as biological tissue scatter light in random, yet deterministic, ways. Wavefront shaping can reverse the effects of scattering to enable deep-tissue microscopy. Such methods require either invasive access to the internal field or the computational solving of an inverse problem. However, calculating the coherent field on a scale relevant to microscopy remains excessively demanding for consumer hardware. Here we show how a recurrent neural network can mirror Maxwell’s equations without training. By harnessing public machine learning infrastructure, the light-field throughout a 6mmarea or 1103µm3 volume can be calculated in 16 minutes. The elimination of the training phase cuts the calculation time and, importantly, it ensures a fully deterministic solution, free of training bias. We integrated our method with an open-source electromagnetic solver. This enables any researcher with an internet connection to calculate complex light-scattering in volumes that are larger by two orders of magnitude.
Original languageEnglish
Place of PublicationCornell University
PublisherarXiv
Number of pages7
DOIs
Publication statusPublished - 1 Aug 2022

Keywords

  • Optics
  • Computer Science
  • Simulation model
  • Neural networks (Computer)

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