Dynamics of confined crowds modelled using Entropic Stochastic Resonance and Quantum Neural Networks

Abstract

We present a new approach to modelling dynamics of confined crowds driven by Entropic Stochastic Resonance (ESR). The standard approach is to model confined Brownian particles using overdamped Langevin equations and corresponding linear, real-time, Fokker-Planck equations for Probability Density Functions (PDFs). Instead, we propose a new approach based on a set of (weakly or strongly) coupled Quantum Neural Networks (QNNs), which are self-organised, complex-valued nonlinear Schrodinger equations with unsupervised Hebbian-type learning. Utilising the full power of nonlinear analysis in the complex-plane, the new approach promises to be ideal for any kind of two-dimensional terrains. Besides, instead of over-simplistic Brownian particles, the new approach allows us to model crowds consisting of rigid-body-type agents.