Figure 2


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Structure of the envisioned NN architecture. (a) Training of a single-layer Model-NN. Training data consists of 2k uniformly distributed random SLM phase masks and the corresponding intensity images measured at the MPLC output. This generates a digital twin of the MPLC setup. (b) Another single-layer called Actor-NN is trained on 2k intensity images according to the EMNIST data set. The Actor-NN is used to predict phase masks, which are the input for the Model-NN. When training the Actor-NN, the Model-NN is fixed. (c) The predicted phase mask of the trained Actor-NN is applied to the SLM and the intensity is measured. We achieve a correlation of Γ = 0.65 compared to the Model-NNs predicted intensity.

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