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Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
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Deep learning-based vortex decomposition and switching based on fiber vector eigenmodes.

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Summary

Researchers developed a novel method using deep learning to accurately decompose and control structured optical fields like cylindrical vector (CV) and orbital angular momentum (OAM) modes, achieving high purity and fast switching capabilities.

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deep learningmode decompositionvector eigenmodesvortex switching

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Area of Science:

  • Optics and Photonics
  • Quantum Information Science

Background:

  • Structured optical fields, including cylindrical vector (CV) and orbital angular momentum (OAM) modes, are crucial for advanced optical applications due to their unique polarization and phase properties.
  • Intelligent generation and precise control of these complex optical modes remain a significant challenge in the field.

Purpose of the Study:

  • To demonstrate a novel simulation and experimental approach for decomposing CV and OAM modes.
  • To achieve accurate and efficient characterization of structured optical fields using multi-view intensity projections and deep learning.

Main Methods:

  • Utilized multi-view images of projected intensity distributions to reconstruct optical field properties.
  • Employed a deep learning-based stochastic parallel gradient descent (SPGD) algorithm for modal decomposition and field retrieval.
  • Incorporated interference patterns and quarter-wave plates for phase confirmation and polarization analysis.

Main Results:

  • Successfully decomposed CV and OAM modes with high accuracy (average error 0.416%) and efficiency (retrieval time 1.32 s).
  • Achieved high purity for decomposed CV modes, reaching up to 99.5%.
  • Demonstrated fast switching of vortex modes by electrically controlling polarization, enabling diverse CV mode generation.

Conclusions:

  • The developed method offers a convenient and accurate way to characterize structured optical fields, including their modal proportions.
  • Findings contribute to a deeper understanding of CV and OAM modes, with potential applications in information coding and quantum computation.