Large Fluctuations in Stochastically Perturbed Nonlinear Systems: Applications in Computing, Engineering, and Economic Modelling

Abstract

The stochastically perturbed nonlinear systems constitute a basis of inquiry in complex dynamics in various fields in the real world; they have an application in the field of computational basis, building and anomaly in the engineering system, economic modeling. These kinds of systems are the ones that exhibit sensitivity to the external noises and internal perturbations that lead to the phenomenon like bifurcations, metastability and infrequent but important transitions. Large fluctuations in such systems are relevant in the study that can be used to predict occurrence of failures, determine optimal system robustness and establish adaptive control. A multidisciplinary study on the notion of large fluctuations dynamics in stochastically driven nonlinear systems with their practical applications in determining computing reliability, control engineering and modeling the dynamics of economic resilience are presented in this paper. Having combined the analytical methods of stochastic calculus with, on the one hand, numerical methods of solution through Monte Carlo and Langevin simulations, and on the other hand, real-life case studies, we excavate how fluctuation-driven instability may manifest itself as both a threat and an asset in improving the system. Our findings will inspire people to consider fluctuation-aware design in system architecture and to propose a framework to simulate, forecast and attempt to reduce noise-induced transitions. We present the qualitative modeling of fluculation theory and quantitative analysis that the fluculation theory can have to add value to engineering and economic systems in terms of forecasting, control precision and policy robustness.