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Points-connecting neural network ray tracing.
Optics Letters
|September 1, 2021
Summary
Unsupervised neural network ray tracing (NNRT) calculates light ray paths in gradient-index media. This method accurately traces ideal paths between two points without needing initial light ray direction, demonstrated using Maxwell
Area of Science:
- Optics and Photonics
- Computational Physics
- Artificial Intelligence
Background:
- Calculating light ray paths in gradient-index media is complex.
- Traditional methods may require initial ray direction or detailed medium properties.
- Neural networks offer a novel approach to solving complex optical problems.
Purpose of the Study:
- To introduce an unsupervised neural network ray tracing (NNRT) method.
- To enable calculation of light ray paths between specified points in gradient-index media.
- To demonstrate the efficacy of NNRT using a known optical system.
Main Methods:
- Developed an unsupervised neural network architecture for ray tracing.
- Implemented the NNRT to find light ray paths connecting two arbitrary points.
- Utilized Maxwell's fisheye lens with a spherical gradient-index as a test case.
Main Results:
- NNRT successfully calculated light ray paths between given points.
- The method determined paths without prior knowledge of ray direction.
- NNRT-traced rays followed ideal paths through the gradient-index medium.
Conclusions:
- Unsupervised neural network ray tracing is a viable method for gradient-index media.
- NNRT provides accurate light ray path calculations between specified points.
- The demonstrated accuracy suggests potential applications in optical design and simulation.
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