Optimization of deformable mirror actuator geometry using machine learning methods
Oleg Soloviev (TU Delft - Mechanical Engineering)
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Abstract
The ability of a membrane deformable mirror to accurately reproduce a predefined range of aberrations is strongly affected by the geometry of its actuator layout. In this presentation, we consider ways to formalize the problem of finding the optimal actuator geometry and show how algorithms from image processing and machine learning can be applied. We illustrate the approach through a case study of a 79-channel membrane deformable mirror developed for the 14AMI project.