A physics-informed neural network method for predicting maximum pitting corrosion depth in pipelines

Key Points

• Maximum pitting corrosion depth can be predicted accurately using a physics-informed neural network method, improving pipeline safety.
• A key finding includes that the model reduces prediction errors significantly compared to traditional methods, enhancing corrosion assessments.
• Analysis performed with a physics-informed approach leverages data and physical laws, enabling more accurate corrosion modeling in real-time.
• These findings highlight a potential shift in monitoring pipeline integrity, warranting further validation in real-world scenarios.