Transition Graphs of Interacting Hysterons: Structure, Design, Organization and Statistics
Margot H. Teunisse, Martin van Hecke
TL;DR
This work develops a general framework for interacting hysterons by linking transition graphs (t-graphs) to microscopic, state-dependent switching fields $U_i^\pm(S)$. It introduces scaffolds to organize the combinatorial space and derives design inequalities that impose a partial order on the switching fields, enabling systematic realizability tests of t-graphs. By constructing all t-graphs for small numbers of hysterons ($n=2,3$) through scaffolds and finite binary trees, the authors quantify the design-space volume and show how avalanches and Garden-of-Eden states shape memory pathways. The framework enables rational design of memory effects in frustrated materials and provides a path toward extended models and finite-state-machine representations of dynamic responses, with practical implications for metamaterials and driven disordered systems.
Abstract
Transition graphs capture the memory and sequential response of multistable media, by specifying their evolution under external driving. Microscopically, collections of bistable elements, or hysterons, provide a powerful model for these materials, with recent work highlighting the crucial role of hysteron interactions. Here, we introduce a general framework that links transition graphs and the microscopic parameters of interacting hysterons. We first introduce a systematic framework, based on so-called scaffolds, which structures the space of transition graphs and provides tools to deal with their combinatorial explosion. We then connect the topology of transition graphs to partial orders of the microscopic parameters. This allows us to understand the statistical properties of transition graphs, as well as determine whether a given graph is realizable, i.e. compatible with the hysteron framework. Our approach paves the way for a deeper theoretical understanding of memory effects in complex media and opens a route to rationally design pathways and memory effects in materials.
