Optical Wavefront Prediction with Reservoir Computing
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Summary
A new method used to estimate the eigenvalues that comprise wavefront perturbations over a wide, spatial field is described, using a recurrent neural network trained using a posteriori wavefront ensembles from multiple point-source objects to predict aberrations caused by atmospheric turbulence.
- Type
- article
- Published
- 2010-01-01
- Cited by
- 2
- References
- 139
- Access
- Open access
- OpenAlex
- https://openalex.org/W51440152
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:59740671
Keywords
Wavefront, Computer science, Geology, Remote sensing, Optics
References
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- Continuous and discrete signal and system analysis
- Inverse problem theory - and methods for model parameter estimation
- Machine learning: Discriminative and generative
- Practical neural network recipes in C
- Field Guide to Adaptive Optics
- Introduction to Adaptive Optics
- Direct adaptive control: an echo state network and genetic algorithm approach
- Digital Picture Processing
- Linear Spatio-Temporal Prediction for Adaptive Optics Wavefront Reconstructors
- Reconstruction of anisoplanatic adaptive optics images
- Handbook of CCD Astronomy
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