Supervised Learning in Physical Networks: From Machine Learning to Learning Machines
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36 MATERIALS SCIENCE↗
Engineering topics
Hexner, Daniel
Publications and source records attributed to Hexner, Daniel.
Periodic training of creeping solids
Significance It is well appreciated that many disordered materials deform their shape irreversibly (plastically) under an external load (e.g., memory foam). Here, we show that this plasticity can be exploited to train materials to develop novel elastic responses by straining them periodically. By applying different periodic strains to a common viscoelastic material, we are able to design a number of different responses. These include a maximally negative Poisson’s ratio, bistable behavior, and nonlocal bond-specific responses. In contrast to computer-aided design, we rely on plasticity to self-organize the system in response to local stresses. This approach shows promise to achieve an unprecedented control over behavior at large strains well beyond the linear-response regime.
36 MATERIALS SCIENCE↗
Effect of directed aging on nonlinear elasticity and memory formation in a material
Disordered solids often change their elastic response as they slowly age. Using experiments and simulations, we study how aging disordered planar networks under an applied stress affects their nonlinear elastic response. We are able to modify dramatically the elastic properties of our systems in the nonlinear regime. Using simulations, we study two models for the microscopic evolution of properties of such a material; the first considers changes in the material strength, while the second considers distortions in the microscopic geometry. Both models capture different aspects of the experiments including the encoding of memories of the aging history of the system and the dramatic effects on the material’s nonlinear elastic properties. Our results demonstrate how aging can be used to create complex elastic behavior in the nonlinear regime.
36 MATERIALS SCIENCE↗