  Resilience engineering studies the ability of a system to survive and recover
from disruptive events, which finds applications in several domains. Most
studies emphasize resilience metrics to quantify system performance, whereas
recent studies propose statistical modeling approaches to project system
recovery time after degradation. Moreover, past studies are either performed on
data after recovering or limited to idealized trends. Therefore, this paper
proposes three alternative neural network (NN) approaches including (i)
Artificial Neural Networks, (ii) Recurrent Neural Networks, and (iii)
Long-Short Term Memory (LSTM) to model and predict system performance,
including negative and positive factors driving resilience to quantify the
impact of disruptive events and restorative activities. Goodness-of-fit
measures are computed to evaluate the models and compared with a classical
statistical model, including mean squared error and adjusted R squared. Our
results indicate that NN models outperformed the traditional model on all
goodness-of-fit measures. More specifically, LSTMs achieved an over 60\% higher
adjusted R squared, and decreased predictive error by 34-fold compared to the
traditional method. These results suggest that NN models to predict resilience
are both feasible and accurate and may find practical use in many important
domains.

Systems and Control;Machine Learning;Performance;Systems and Control