Low rattling: A predictive principle for self-organization in active collectives

Abstract

In classical statistical mechanics, the deterministic dynamics of a many-body system are replaced by a probabilistic description. Chvykov et al. work toward a similar description for the nonequilibrium self-organization of collectives of active particles. In these systems, continuously input energy drives localized fluctuations, but larger-scale ordering can emerge, such as in the flight of a flock of birds. A key concept in their theory is the importance of rattling, whereby ordered patterns emerge through local collisions between neighbors at specific frequencies. The authors demonstrate this behavior using a set of flapping robots and produce related simulations of the robot behavior.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2021
Source ID
10.1126/science.abc6182

Entities

People

  • Akash Vardhan
  • Alexander Samland
  • Daniel I. Goldman
  • Jeremy L England
  • Kurt Wiesenfeld
  • Pavel Chvykov
  • Thomas A Berrueta
  • Todd D Murphey
  • William Savoie

Organizations

  • Army Research Office
  • GSK
  • Georgia Tech
  • James S. McDonnell Foundation
  • Massachusetts Institute of Technology
  • National Science Foundation
  • Northwestern University

Tags

Fields of Study

  • Physics

Readers

  • Neural Network Machine Learning.
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control