APS March Meeting 2024

Bifurcation instructed design of multistate machines


Systems composed of many interacting elements that collaboratively generate a function, such as meta-material robots, proteins and neural-networks are often not amenable to compartmentalized design: where individual modules each perform a distinct sub-function, and are then composed to create the desired complex function. I will describe an alternative design paradigm where the function of machines arises from interactions of all the machine components, and the operation of the machine is organized by a bifurcation of multiple equilibria. These special points allow for robustly cycling between multiple distinct states by a small change of only a few control parameters. I will illustrate this approach on a simple magneto elastic machine, and discuss its implications for the design of microscopic robots and protein based machines. *This work was financially supported by NSF Grant DMREF-89228, NSF Grant EFRI-1935252, NSF Grant CBET-2010118, Cornell Center for Materials Research DMR-1719875, and by Air Force Office of Scientific Research Grant MURI: FA9550-16-1-0031, the Cornell Laboratory of Atomic and Solid State Physics and Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, and an NSF Graduate Research Fellowship Grant No. DGE-2139899