The most reliable models do not, however, merely predict just predict a quantity but also allow assessment of the model's uncertainty. Emblematic of this is the work by Tong et al. , who introduced ADLSTM-MC, a hybrid predictive model using adaptive dropout long short term memory (LSTM) with Monte Carlo simulations. This approach, which requires minimal training data, enhances robustness through Bayesian-optimized dropout rates and improves the remaining useful life of two types of LIBs. In a correlative study , a recurrent autoregressive deep ensemble network with aleatoric and epistemic uncertainties was developed along with saliency analysis to assess the impact of input parameters on output prediction. This provided an intuitive understanding of feature importance.