Efficient Interdependent Systems Recovery Modeling with DeepONets

TL;DR

This paper demonstrates that DeepONets can efficiently model the recovery process of interdependent critical infrastructure systems, significantly reducing computational costs while maintaining accuracy with limited training data.

Contribution

The paper introduces the novel application of DeepONets to model interdependent systems recovery, showing their effectiveness with minimal training data.

Findings

DeepONets accurately predict recovery in interdependent systems.

Model performs well with limited training data.

Significant reduction in computational expense.

Abstract

Modeling the recovery of interdependent critical infrastructure is a key component of quantifying and optimizing societal resilience to disruptive events. However, simulating the recovery of large-scale interdependent systems under random disruptive events is computationally expensive. Therefore, we propose the application of Deep Operator Networks (DeepONets) in this paper to accelerate the recovery modeling of interdependent systems. DeepONets are ML architectures which identify mathematical operators from data. The form of governing equations DeepONets identify and the governing equation of interdependent systems recovery model are similar. Therefore, we hypothesize that DeepONets can efficiently model the interdependent systems recovery with little training data. We applied DeepONets to a simple case of four interdependent systems with sixteen states. DeepONets, overall, performed satisfactorily in predicting the recovery of these interdependent systems for out of training sample data when compared to reference results.