What is a Physics-Informed Graph Neural Network (PI-GNN)?

A PI-GNN is a graph neural network where the graph represents the asset topology (nodes = sensor measurements, edges = physical connections between monitoring points) and training is constrained by electrochemical equations including Butler-Volmer kinetics and Pourbaix thermodynamic stability diagrams.

How does CorrosionAI quantify prediction uncertainty?

CorrosionAI uses Bayesian Monte Carlo inference (100 stochastic forward passes) to generate a full probability distribution for each prediction, quantifying both aleatoric uncertainty (inherent data noise) and epistemic uncertainty (model knowledge gaps).

The Science Behind CorrosionAI

Q: How does physics-informed AI predict corrosion?

Physics-informed AI embeds Butler-Volmer kinetics and Pourbaix diagrams directly into the neural network's loss function. This ensures predictions are both data-accurate and physically plausible, achieving >95% accuracy even when extrapolating to conditions never seen in training data.

Physics-informed machine learning for unprecedented accuracy in corrosion prediction

How does physics-informed AI predict corrosion? CorrosionAI's proprietary PI-GNN (Physics-Informed Graph Neural Network) architecture embeds fundamental electrochemical laws — Butler-Volmer reaction kinetics and Pourbaix thermodynamic stability diagrams — directly into the neural network's learning process. Unlike pure machine learning corrosion models that treat the problem as a black-box regression, PI-GNN ensures every prediction obeys the physics of electrochemical corrosion. The result: >95% accuracy with calibrated uncertainty bounds, even when extrapolating to conditions never seen in training data.

Why Physics-Informed Matters

Electrochemical constraints as guardrails. Butler-Volmer + Pourbaix embedded in loss function prevent physically impossible predictions.

Graph structure captures spatial reality. Pipeline = graph (nodes = sensors, edges = physical connections). Galvanic coupling, flow-accelerated corrosion, CP shielding all modeled.

Less data, more accuracy. >95% accuracy with 100-500 samples vs 10,000+ for pure ML.

Reliable uncertainty. Bayesian Monte Carlo provides calibrated confidence intervals for every prediction.

Explainable by design. SHAP values + attention maps for regulatory compliance (API 580/581).

Key Technologies

Advanced capabilities that set CorrosionAI apart

Physics-Informed Neural Networks

PINN architecture that respects physical laws while learning from data.

Self-Calibrating Sensors

AI-powered calibration that compensates for drift automatically.

Uncertainty Quantification

Confidence intervals with every prediction for risk-informed decisions.

Explainable AI

Transparent models with clear reasoning for every prediction.

PI-GNN vs. Industry Corrosion Models

How CorrosionAI's physics-informed approach compares to traditional and pure ML models.

Criterion	CorrosionAI PI-GNN	de Waard-Milliams	NORSOK M-506	FreeCorp	Pure ML (XGBoost/LSTM)
Model Type	Physics-informed GNN	Empirical	Semi-empirical	Mechanistic	Data-driven
CO2 Corrosion	Yes	Yes	Yes	Yes	If data exists
H2S Corrosion	Yes, 10 ppm	Limited	No	Yes	Depends on data
Pitting Prediction	Probabilistic (Bayesian)	No	No	Limited	Unreliable
Field Accuracy	>95%	60-70%	65-75%	75-85%	75-85%
Extrapolation	High (physics-constrained)	Poor	Poor	Moderate	Very poor
Uncertainty Quantification	Bayesian Monte Carlo	None	None	None	Poorly calibrated
Explainability	SHAP + attention maps	Transparent (simple eq.)	Transparent	Moderate	Black box
Spatial Correlation	Graph topology	Independent points	Independent	Independent	Independent
Training Data Required	~100-500 samples	None (empirical)	None	None	10,000+
Real-Time Capable	Yes (SCADA)	Manual only	Manual only	Batch	With custom pipeline

Technical Specifications

95%+

Accuracy

10 ppm

Detection

<60s

Analysis

1-20

Year Forecast

Frequently Asked Questions

Ready to See CorrosionAI in Action?

Schedule a demo to discover our physics-informed AI.

Request Demo