Uncovering multifunctional mechano-intelligence in and through phononic
  metastructures harnessing physical reservoir computing
Yuning Zhang Aditya Deshmukh K. W. Wang
The recent advances in autonomous systems have prompted a strong demand for
the next generation of adaptive structures and materials to possess more
built-in intelligence in their mechanical domain, the so-called
mechano-intelligence (MI). Previous MI attempts mainly focused on specific
designs and case studies to realize limited aspects of MI, and there is a lack
of a systematic foundation in constructing and integrating the different
elements of intelligence in an effective and efficient manner. Here, we propose
a new approach to create the needed foundation in realizing integrated
multifunctional MI via a physical reservoir computing (PRC) framework. That is,
to concurrently embody computing power and the various elements of
intelligence, namely perception, decision-making, and commanding, directly in
the mechanical domain, advancing from conventional adaptive structures that
rely solely on add-on digital computers and massive electronics to achieve
intelligence. As an exemplar platform, we construct a mechanically intelligent
phononic metastructure with the integrated elements of MI by harnessing the PRC
power hidden in their high-degree-of-freedom nonlinear dynamics. Through
analyses and experimental investigations, we uncover multiple adaptive
structural functions ranging from self-tuning wave controls to wave-based logic
gates. This research will provide the basis for creating future new structures
that would greatly surpass the state of the art - such as lower power
consumption, more direct interactions, and much better survivability in harsh
environment or under cyberattacks. Moreover, it will enable the addition of new
functions and autonomy to systems without overburdening the onboard computers.