Additive Noise-Induced System Evolution (ANISE) - Hutt, Axel (Frontiers in Applied Mathematics and Statistics, 2022)
Additive noise has been known for a long time to not change a systems stability. The dis- covery of stochastic and coherence resonance in nature and their analytical description has started to change this view in the last decades. The detailed studies of stochastic bifurcations in the last decades have also contributed to change the original view on the role of addi- tive noise. The present work attempts to put these pieces of work in a broader context by proposing the research direction ANISE as a perspective in the research field. ANISE may embrace all studies that demonstrates how additive noise tunes a systems evolution be- yond just scaling its magnitude. The article provides two perspective directions of research. The first perspective is the generalization of previous studies on the stationary state sta- bility of a stochastic random network model subjected to additive noise. Here the noise induces novel stationary states. A second per- spective is the application of subgrid-scale modeling in stochastic random network model. It is illustrated how numerical parameter estima- tion complements and extends subgrid-scale modeling and render it more powerful.