Physics-informed failure prediction in disordered systems sharing a common resource

Abstract

We study the progressive degradation of disordered systems that experience multiple intermediate failures and equilibrations before collapsing while sharing a common resource. The system is modelled using a generalized Fibre Bundle framework, wherein individual elements fail upon exceeding their local thresholds, and their load is redistributed among surviving elements according to a prescribed load-sharing scheme. We employ two classes of disorder distributions: the two-parameter Weibull and a more flexible custom distribution. To predict the ultimate tensile strength (UTS) and critical burst size which characterize system failure in this model—we employ Artificial Neural Networks (ANNs) informed by theoretical expressions rooted in statistical physics. Our investigation shows that the predictive performance of ANNs is significantly improved (from 83% to 99%) by our Physics informed theoretical predictors. This approach reduces the need for large-scale simulations and is a more efficient way to estimate the reliability of such complex disordered systems.