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Area of Science:

  • Physics
  • Materials Science
  • Complex Systems

Background:

  • Coupled binary element models capture memory effects in dissipative materials.
  • Random interactions lead to self-loops, hindering statistical analysis and dissipative dynamics.

Purpose of the Study:

  • Investigate the origin of self-loops in dissipative systems.
  • Develop methods to suppress self-loops for effective statistical studies.
  • Unify descriptions of passive and active multistable materials.

Main Methods:

  • Analysis of energy injection and limit cycles in physical systems.
  • Introduction of novel interaction ensembles.
  • Development of hysteron models.

Main Results:

  • Self-loops originate from energy injection and limit cycles.
  • New interaction ensembles effectively suppress or eliminate self-loops.
  • Enabled statistical studies of memory in large dissipative systems.

Conclusions:

  • Suppression of self-loops is crucial for studying memory in dissipative systems.
  • The developed models offer a unified framework for multistable materials.
  • Opens new avenues for understanding transient responses and sequential computing.