Work

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

Year: 2024

Type: conference-paper

Abstract: 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.

DOI: 10.3390/engproc2024069205

Authors: Ivo Daniel, Gopinathan R. Abhijith, J. Nathan Kutz, Avi Ostfeld, Andrea Cominola

Institutions: Einstein Center Digital Future, Technische Universität Berlin, Indian Institute of Technology Kanpur, University of Washington, Technion – Israel Institute of Technology

Language: English

Topic: Water Systems and Optimization

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

OpenAlex ID: W4403775361

FWCI: 8.083

Cites: 6

Cited by: 5

Related to: 10