A physics-defined recurrent neural network to compute coherent light wave scattering on the millimetre scale

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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 ability to numerically compute it. 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, such \emph{Scattering Network} can compute the 633nm -wavelength light field throughout a 25mm2 or 1763μm3 scattering volume. The elimination of the training phase cuts the calculation time to a minimum and, importantly, it ensures a fully deterministic solution, free of any training bias. The integration with an open-source electromagnetic solver enables any researcher with an internet connection to calculate complex light-scattering in volumes that are larger by two orders of magnitude
Original languageUndefined/Unknown
Place of PublicationCornell University
PublisherarXiv
Number of pages8
DOIs
Publication statusPublished - 8 Dec 2022

Keywords

  • physics.comp-ph
  • physics.optics

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