  We experimentally solve the problem of maximizing capacity under a total
supply power constraint in a massively parallel submarine cable context, i.e.,
for a spatially uncoupled system in which fiber Kerr nonlinearity is not a
dominant limitation. By using multi-layer neural networks trained with
extensive measurement data acquired from a 12-span 744-km optical fiber link as
an accurate digital twin of the true optical system, we experimentally maximize
fiber capacity with respect to the transmit signal's spectral power
distribution based on a gradient-descent algorithm. By observing convergence to
approximately the same maximum capacity and power distribution for almost
arbitrary initial conditions, we conjecture that the capacity surface is a
concave function of the transmit signal power distribution. We then demonstrate
that eliminating gain flattening filters (GFFs) from the optical amplifiers
results in substantial capacity gains per Watt of electrical supply power
compared to a conventional system that contains GFFs.
