  We develop a practical machine learning approach to determine the disorder
landscape of Majorana nanowires by using training of the conductance matrix and
inverting the conductance data in order to obtain the disorder details in the
system. The inversion carried out through machine learning using different
disorder parametrizations turns out to be unique in the sense that any input
tunnel conductance as a function of chemical potential and Zeeman energy can
indeed be inverted to provide the correct disorder landscape. Our work opens up
a qualitatively new direction of directly determining the topological invariant
and the Majorana wave-function structure corresponding to a transport profile
of a device using simulations that quantitatively match the specific
conductance profile. In addition, this also opens up the possibility for
optimizing Majorana systems by figuring out the (generally unknown) underlying
disorder only through the conductance data. An accurate estimate of the
applicable spin-orbit coupling in the system can also be obtained within the
same scheme.
