Abstract Following the Fukushima Daiichi accident, enhancing the safety of nuclear power
plants has become the priority mission for the future of nuclear energy. Probabilistic safety …

To obtain more accurate results of probabilistic safety assessment (PSA), it is necessary to
reflect more complete dynamics of nuclear power plants. In analyzing these more realistic …

In the last few years, deep learning in neural networks demonstrated impressive successes
in the areas of computer vision, speech and image recognition, text generation, and many …

Many studies have proposed deep learning models to diagnose faults and predict accidents
in nuclear power reactors. However, the training data in these studies are deterministic, and …

Deep learning algorithms provide plausible benefits for efficient prediction and analysis of
nuclear reactor safety phenomena. However, research works that discuss the critical …

Post-processing using deep learning algorithms can be conducted to improve accuracy of
hydrologic predictions and quantify their uncertainty. In this paper, a revised version of the …

In this work, a multivariate time-series machine learning meta-model is developed to predict
the transient response of a typical nuclear power plant (NPP) undergoing a steam generator …

Featured Application This research study can be utilized to improve the data assimilation
process and uncertainty quantification analysis. Abstract The reflooding phase, a crucial …

Several studies have proposed machine learning models to diagnose and predict accidents
in nuclear power reactors. However, the training data in these studies are deterministic, and …

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential
to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating …