An ISM-based virtual fault tree and a graph convolutional network are combined to predict Fussell-Vesely importance on two small nuclear subsystems, with claims of millisecond inference and high accuracy.
Reinforcement Learning and Graph Neural Networks for Probabilistic Risk Assessment
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abstract
This paper presents a new approach to the solution of Probabilistic Risk Assessment (PRA) models using the combination of Reinforcement Learning (RL) and Graph Neural Networks (GNNs). The paper introduces and demonstrates the concept using one of the most popular PRA models - Fault Trees. This paper's original idea is to apply RL algorithms to solve a PRA model represented with a graph model. Given enough training data, or through RL, such an approach helps train generic PRA solvers that can optimize and partially substitute classical PRA solvers that are based on existing formal methods. Such an approach helps to solve the problem of the fast-growing complexity of PRA models of modern technical systems.
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A Hybrid Real-Time Framework for Efficient Fussell-Vesely Importance Evaluation Using Virtual Fault Trees and Graph Neural Networks
An ISM-based virtual fault tree and a graph convolutional network are combined to predict Fussell-Vesely importance on two small nuclear subsystems, with claims of millisecond inference and high accuracy.