Synchronization, Symmetries and Information Processing with Dynamical Networks
Abstract
Networks of dynamical systems are ubiquitous in communications, power grids as well as neuronal networks in the brain. However, large networks of dynamical nodes that are well specified and controlled are very rare in the laboratory. Network experiments that are precise and work on fast time scales are a most valuable way to test concepts and ideas that are increasingly important in the real world, in commerce, medicine, industrial, and defense environments. While we have earlier pioneered the use of liquid crystal spatial light modulators for creating complex networks, in a recent experimental breakthrough, we have discovered how to implement arbitrary complex network topologies with a time-multiplexed delay optoelectronic feedback system, using optical modulation techniques and field programmable gate arrays.This discovery [Hart 2017(a)] has enabled us to test predictions on equitable partitions in collaboration with Dr. Lou Pecora of the Naval Research Lab and Professor Francesco Sorrentino of the University of New Mexico [Siddique (2018)]. It has also been applied to the topological control of synchronization patterns and revealed the surprising possibility of trading symmetry for extending the stability of patterns over larger coupling parameter regimes [Hart 2019(b)]. The most recent direct" application of this technique has been the creation of ""A poor man s coherent Ising machine based on opto-electronic feedback syste""ms for solving optimization problems"" [Bohm 2019].Here we propose the use of this method for (i) developing new hardware technique"s for information processing and reservoir computing and (ii) experiments to observe and characterize a novel kind of chaos (named Laminar Chaos) that has been discovered [Muller (2018)] in theoretical and numerical studies, and extending investigations of nonlinear Langevin equations with variable time-delays and noise strengths (iii) studying spontaneous switching of chimera states in networks, which could lead to noise detectors of unprecedented sensitivity.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 11, 2020
- Source ID
- N000142012139
Entities
People
- Rajarshi Roy
Organizations
- Office of Naval Research
- United States Navy
- University of Maryland