  We demonstrate a Physics-informed Neural Network (PINN) based model for
real-time health monitoring of a heat exchanger, that plays a critical role in
improving energy efficiency of thermal power plants. A hypernetwork based
approach is used to enable the domain-decomposed PINN learn the thermal
behavior of the heat exchanger in response to dynamic boundary conditions,
eliminating the need to re-train. As a result, we achieve orders of magnitude
reduction in inference time in comparison to existing PINNs, while maintaining
the accuracy on par with the physics-based simulations. This makes the approach
very attractive for predictive maintenance of the heat exchanger in digital
twin environments.
