  Perturbations in complex media, due to their own dynamical evolution or to
external effects, are often seen as detrimental. Therefore, a common strategy,
especially for telecommunication and imaging applications, is to limit the
sensitivity to those perturbations in order to avoid them. Here, we instead
consider crashing straight into them in order to maximize the interaction
between light and the perturbations and thus produce the largest change in
output intensity. Our work hinges on the innovative use of tensor-based
techniques, presently at the forefront of machine learning explorations, to
study intensity-based measurements where its quadratic relationship to the
field prevents the use of standard matrix methods. With this tensor-based
framework, we are able to identify the optimal crashing channel which maximizes
the change in its output intensity distribution and the Fisher information
encoded in it about a given perturbation. We further demonstrate experimentally
its superiority for robust and precise sensing applications. Additionally, we
derive the appropriate strategy to reach the precision limit for
intensity-based measurements leading to an increase in Fisher information by
more than four orders of magnitude with respect to the mean for random
wavefronts when measured with the pixels of a camera.
