romeo-5/nuclear-power-plant-accident-diagnosis-and-root-cause-analysis

This repository is an ML research pipeline for diagnosing faults in simulated pressurized-water-reactor sensor time series. The user-facing workflow is to obtain and preprocess NPPAD data, train and evaluate anomaly detectors and accident classifiers, and train a physics-informed digital twin. Classification interpretability and uncertainty support diagnosis; a documented cross-validation script combines classifier outcomes with twin physics residuals. Edges reflect README workflow statements and shown/import-indexed dependencies; unsampled source limits finer runtime wiring.