Physics-Informed Graph Neural Networks for Water Distribution Systems

• URL: http://arxiv.org/abs/2403.18570v1
• Date: Wed, 27 Mar 2024 13:51:26 GMT
• Title: Physics-Informed Graph Neural Networks for Water Distribution Systems
• Authors: Inaam Ashraf, Janine Strotherm, Luca Hermes, Barbara Hammer,
• Abstract summary: Water distribution systems (WDS) are an integral part of critical infrastructure which is pivotal to urban development. We propose a physics-informed deep learning (DL) model, for hydraulic state estimation in WDS. Our model uses hydraulic principles to infer two additional hydraulic state features in the process of reconstructing the available ground truth feature.
• Score: 3.9675504428227457
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Water distribution systems (WDS) are an integral part of critical infrastructure which is pivotal to urban development. As 70% of the world's population will likely live in urban environments in 2050, efficient simulation and planning tools for WDS play a crucial role in reaching UN's sustainable developmental goal (SDG) 6 - "Clean water and sanitation for all". In this realm, we propose a novel and efficient machine learning emulator, more precisely, a physics-informed deep learning (DL) model, for hydraulic state estimation in WDS. Using a recursive approach, our model only needs a few graph convolutional neural network (GCN) layers and employs an innovative algorithm based on message passing. Unlike conventional machine learning tasks, the model uses hydraulic principles to infer two additional hydraulic state features in the process of reconstructing the available ground truth feature in an unsupervised manner. To the best of our knowledge, this is the first DL approach to emulate the popular hydraulic simulator EPANET, utilizing no additional information. Like most DL models and unlike the hydraulic simulator, our model demonstrates vastly faster emulation times that do not increase drastically with the size of the WDS. Moreover, we achieve high accuracy on the ground truth and very similar results compared to the hydraulic simulator as demonstrated through experiments on five real-world WDS datasets.

Related papers

• Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks [93.38375271826202]
  We present a method to improve generalization and robustness to distribution shifts in sim-to-real visual quadrotor navigation tasks. We first build a simulator by integrating Gaussian splatting with quadrotor flight dynamics, and then, train robust navigation policies using Liquid neural networks. In this way, we obtain a full-stack imitation learning protocol that combines advances in 3D Gaussian splatting radiance field rendering, programming of expert demonstration training data, and the task understanding capabilities of Liquid networks.
  arXiv  Detail & Related papers  (2024-06-21T13:48:37Z)
• Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks [1.8073031015436376]
  Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks. Fully-connected neural networks (NNs) are potential surrogate models, but may suffer from low interpretability and efficiency in fitting complex targets. This work proposes a GNN-based surrogate of the flow routing model for the hydraulic prediction problem of drainage networks.
  arXiv  Detail & Related papers  (2024-04-16T07:08:04Z)
• Bridging the Sim-to-Real Gap with Bayesian Inference [53.61496586090384]
  We present SIM-FSVGD for learning robot dynamics from data. We use low-fidelity physical priors to regularize the training of neural network models. We demonstrate the effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a high-performance RC racecar system.
  arXiv  Detail & Related papers  (2024-03-25T11:29:32Z)
• Graph Neural Networks for Pressure Estimation in Water Distribution Systems [44.99833362998488]
  Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. We combine physics-based modeling and Graph Neural Networks (GNN), a data-driven approach, to address the pressure estimation problem. Our GNN-based model estimates the pressure of a large-scale WDN in The Netherlands with a MAE of 1.94mH$$O and a MAPE of 7%.
  arXiv  Detail & Related papers  (2023-11-17T15:30:12Z)
• Training Deep Surrogate Models with Large Scale Online Learning [48.7576911714538]
  Deep learning algorithms have emerged as a viable alternative for obtaining fast solutions for PDEs. Models are usually trained on synthetic data generated by solvers, stored on disk and read back for training. It proposes an open source online training framework for deep surrogate models.
  arXiv  Detail & Related papers  (2023-06-28T12:02:27Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator [57.57321628587564]
  We introduce Hybrid Graph Network Simulator (HGNS) for learning reservoir simulations of 3D subsurface fluid flows. HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure. Using an industry-standard subsurface flow dataset (SPE-10) with 1.1 million cells, we demonstrate that HGNS is able to reduce the inference time up to 18 times compared to standard subsurface simulators.
  arXiv  Detail & Related papers  (2022-06-15T17:29:57Z)
• Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning [0.0]
  We propose and demonstrate a new approach for fast and accurate surrogate modelling of urban drainage system hydraulics based on physics-guided machine learning. Our approach reduces simulation times by one to two orders of magnitude compared to a HiFi model. It is thus slower than e.g. conceptual hydrological models, but it enables simulations of water levels, flows and surcharges in all nodes and links of a drainage network.
  arXiv  Detail & Related papers  (2022-05-24T19:44:46Z)
• Echo State Network for two-dimensional turbulent moist Rayleigh-B\'enard convection [0.0]
  We apply an echo state network to approximate the evolution of moist Rayleigh-B'enard convection. We conclude that our model is capable of learning complex dynamics.
  arXiv  Detail & Related papers  (2021-01-27T11:27:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.