Physics-informed neural networks in water and wastewater systems: a critical review
University of Catania · University of Colorado Boulder · +2 more institutions
Abstract
Physics-Informed Neural Networks (PINNs) represent a hybrid modeling paradigm that embeds governing physical laws, expressed as partial differential equations (PDEs), directly into neural network training. This integration enables models to respect fundamental conservation principles while learning from sparse or incomplete data. This review critically examines PINN applications in water and wastewater systems over the period 2014-2024, focusing on drinking water distribution networks, wastewater treatment plants, urban drainage systems, and water treatment processes. The review shows that PINNs excel in inverse problem solving by enabling parameter estimation and system identification from indirect…
Citation impact
- FWCI
- 86.91
- Percentile
- 100%
- References
- 65
Authors
4Topics & keywords
- Artificial neural network
- Extrapolation
- Identification (biology)
- Consistency (knowledge bases)
- Convergence (economics)
- System identification
- Partial differential equation
- Generalization
- Clean water and sanitation