[88] [3] A Parsa, D Wang, CS O'Hern, MD Shattuck, R Kramer-bottiglio, J Bongard, Evolving Programmable Computational Metamaterials, Proceedings Of The 2022 Genetic And Evolutionary Computation Conference (gecco'22), ed. JE Fieldsend, Proceedings Of The 2022 Genetic And Evolutionary Computation Conference (gecco'22):122(8) 2022. [Citations: 3]. [pdf]
Digital signal processors are widely used in today's computers to perform advanced computational tasks. But, the selection of digital electronics as the physical substrate for computation a hundred years ago was influenced more by technological limitations than substrate appropriateness. In recent decades, advances in chemical, physical and material sciences have provided new options. Granular metamaterials are one such promising target for realizing mechanical computing devices. However, their high-dimensional design space and the unintuitive relationship between microstructure and desired macroscale behavior makes the inverse design problem formidable. In this paper, we use multiobjective evolutionary optimization to solve this inverse problem: we demonstrate the design of basic logic gates embedded in a granular metamaterial, and that the designed material can be "reprogrammed" via frequency modulation. As metamaterial design advances, more computationally dense materials may be evolved, amenable to reprogramming by increasingly sophisticated programming languages written in the frequency domain.
