This article presents a machine-learning approach to reconstruct the disorder landscape of Majorana nanowires by training on the tunnelling conductance matrix. Any measured conductance profile can be inverted to uniquely extract disorder parameters, allowing the determination of topological invariants and the structure of Majorana wave-functions. The work opens a new pathway for diagnosing and optimizing Majorana devices directly from experimental conductance data.

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