The use of cascaded neural networks for the reverse design of metamaterials and nanophotons can effectively alleviate the problems caused by one-to-many mapping, but the intermediate layer of the cascaded network is unsupervised learning, and an effective method is needed to limit output range of the intermediate layer.
Multi-layer networks can't and don't alleviate the problems caused by one-to-many mappings. What they do is allow the representation of associations that aren't linearly separable.
Since the output of any unit of an intermediate layer becomes an input to the next layer, the range of input/output values is determined by the activation function of each unit. So it's simple to limit the range-just specify the activation function as desired.
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