Physics-Informed Machine Learning for Universal Surrogate Modelling of Water Quality Parameters in Water Distribution Networks

Modelling and assessing water quality parameters in water distribution networks is essential for providing safe drinking water to end users. While simulation-based modeling approaches rely on costly differentiation for numerical solvers, surrogate models using Artificial Neural Networks (ANNs) can predict solutions with minimal computational effort. In this work, we formulate the idea of a universal surrogate model for predicting water quality dynamics that, once trained, will apply to all water distribution networks. To this end, we adapt the idea of meta-parameterized ANNs to account for variable boundary and initial conditions.