[D] Solving an inverse problem with machine learning where you predict multiple output arrays from a single input array

I am an aquatic optical scientist who has created a massive synthetic dataset of spectral reflectances with paired spectral absorption and backscatter data for multiple aquatic components. I am fairly adept at applying simple ANNs for supervised regression using reflectance as input and singular parameters such as chlorophyll or sediment concentrations as output. What would be the best approach to predict multiple components of spectral data from one reflectance measurement. For example, I have a single reflectance measurement between 400-900 …

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