This project leverages Physics-Informed Neural Networks (PINNs) to model and forecast the thermal performance of power transformers in the Belgian transmission network. By combining physical thermal equations with historical operational and environmental data, the approach enables accurate temperature estimation, early detection of overheating risks, and supports data-driven predictive maintenance strategies.

This project represents a strategic shift from single, general-purpose AI models to an ensemble ecosystem purpose-built for the legal domain. Unlike conventional models that struggle with contractual complexity, this architecture mirrors a legal professional’s reasoning: prioritizing precision, intent comprehension, and rigorous source validation.