  We address the fundamental question of how to optimally probe a scene with
electromagnetic (EM) radiation to yield a maximum amount of information
relevant to a particular task. Machine learning (ML) techniques have emerged as
powerful tools to extract task-relevant information from a wide variety of EM
measurements, ranging from optics to the microwave domain. However, given the
ability to actively illuminate a particular scene with a programmable EM
wavefront, it is often not clear what wavefronts optimally encode information
for the task at hand (e.g., object detection, classification). Here, we show
that by integrating a physical model of scene illumination and detection into a
ML pipeline, we can jointly learn optimal sampling and measurement processing
strategies for a given task. We consider in simulation the example of
classifying objects using microwave radiation produced by dynamic metasurfaces.
By integrating an analytical forward model describing the metamaterial elements
as coupled dipoles into the ML pipeline, we jointly train analog model weights
with digital neural network weights. The learned non-intuitive illumination
settings yield a higher classification accuracy using fewer measurements. On
the practical level, these results are highly relevant to emerging
context-aware systems such as autonomous vehicles, touchless human-interactive
devices or within smart health care, where strict time constraints place severe
limits on measurement strategies. On the conceptual level, our work serves as a
bridge between wavefront shaping and tunable metasurface design on the physical
layer and ML techniques on the processing layer.
