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Multimode optical fibers (MMFs) have gained renewed interest in the pastdecade, emerging as a way to boost optical communication data-rates in thecontext of an expected saturation of current single-mode fiber-based networks.They are also attractive for endoscopic applications, offering the possibilityto achieve a similar information content as multicore fibers, but with a muchsmaller footprint, thus reducing the invasiveness of endoscopic procedures.However, these advances are hindered by the unavoidable presence of disorderthat affects the propagation of light in MMFs and limits their practicalapplications. We introduce here a general framework to study and avoid theeffect of disorder. We experimentally find an almost complete set of opticalchannels that are resilient to disorder induced by strong deformations. Thesedeformation principle modes are obtained by only exploiting measurements forweak perturbations. We explain this effect by demonstrating that, even for ahigh level of disorder, the propagation of light in MMFs can be characterizedby just a few key properties. These results are made possible thanks to aprecise and fast estimation of the modal transmission matrix of the fiber whichrelies on a model-based optimization using deep learning frameworks.

Learning and avoiding disorder in multimode fibers