Physics-Aware ML Models for Pipeline Leak Detection and Quantification

A physics-informed neural network that embeds mass- and momentum-conservation laws directly into the model to detect, locate, and size leaks in multiphase oil-gas-water pipelines. Hitting 95% detection accuracy with ~5 m localization and ~1% leak-rate error, it beat both black-box ML and traditional transient-modeling baselines.