Gradient-based Design of Computational Granular Crystals
Atoosa Parsa Corey S. O'Hern Rebecca Kramer-Bottiglio Josh Bongard
There is growing interest in engineering unconventional computing devices
that leverage the intrinsic dynamics of physical substrates to perform fast and
energy-efficient computations. Granular metamaterials are one such substrate
that has emerged as a promising platform for building wave-based information
processing devices with the potential to integrate sensing, actuation, and
computation. Their high-dimensional and nonlinear dynamics result in nontrivial
and sometimes counter-intuitive wave responses that can be shaped by the
material properties, geometry, and configuration of individual grains. Such
highly tunable rich dynamics can be utilized for mechanical computing in
special-purpose applications. However, there are currently no general
frameworks for the inverse design of large-scale granular materials. Here, we
build upon the similarity between the spatiotemporal dynamics of wave
propagation in material and the computational dynamics of Recurrent Neural
Networks to develop a gradient-based optimization framework for harmonically
driven granular crystals. We showcase how our framework can be utilized to
design basic logic gates where mechanical vibrations carry the information at
predetermined frequencies. We compare our design methodology with classic
gradient-free methods and find that our approach discovers higher-performing
configurations with less computational effort. Our findings show that a
gradient-based optimization method can greatly expand the design space of
metamaterials and provide the opportunity to systematically traverse the
parameter space to find materials with the desired functionalities.