Directed aging, memory, and nature’s greed

Abstract

Disordered materials are often out of equilibrium and evolve very slowly. This allows a memory of the imposed strains or preparation conditions to be encoded in the material. Here we consider "directed aging", where the elastic properties of a material evolve in the direction defined by its aging history. The evolution to a lower-energy configuration is controlled by steepest decent and affects stressed regions differently from unstressed ones. This process can be considered to be a "greedy algorithm" of Nature. Our experiments and simulations illustrate directed aging in examples in which the material's elasticity evolves as a direct consequence of the imposed deformation; the material itself decides how to evolve in order to produce responses that were not present inherently in the material.

Supervised learning through physical changes in a mechanical system

“…1A), which is naturally multistable. Our analysis can be generalized to other disordered mechanical systems, such as elastic networks (16), that are also generically multistable. It was previously shown that creased sheets, such as those of self-folding origami, can be folded into exponentially many discrete folded structures from the flat, unfolded state (17,18).…”

“…Other materials can show a plastic change in stiffness in response to aging under strain, such as ethylene vinyl acetate (EVA) foam (36) and thermoplastic polyurethane (37). EVA was used recently (16) to show such behavior in a mechanical system trained for auxetic response. Another possible method, explored specifically for origami creases (20), controls the crease width (and, hence, stiffness) using photolitography (38).…”

“…Our proposal here relies on a plastic element-namely, crease stiffness. Materials that stiffen or soften with strain have been demonstrated in several contexts (12)(13)(14)(15), including recently in the training of mechanical metamaterials (16). We discuss how learning performance may be affected by limitations on the dynamic range of stiffness and other practical constraints in such materials.…”

Continual Learning of Multiple Memories in Mechanical Networks

“…Our framework can also be implemented on much larger length scales using recent experimental demonstrations of plasticity in EVA (Ethylene-vinyl acetate) foam [38]. Here, learning involves continually changing the stiffness of existing bonds, rather than growing new bonds every time.…”

“…(b) EVA foam-based network changes nonlinear elastic properties when aged under strain (adapted from Ref. [38]). (c) Reversible stress softening in actin networks, a natural form of elastic nonlinearity in grown networks (adapted from Ref.…”