Application of Physics-Informed Neural Networks for Solving the Inverse Advection-Diffusion Problem to Localize Pollution Sources

2025-03-24

AI Summary

This work addresses the inverse advection–diffusion problem for precise pollution source localization under complex meteorological conditions—such as weak/strong winds and multiple emission sources. We propose a physics-informed neural network (PINN)-based method that departs from conventional PINN applications by systematically evaluating the impact of network architecture and hyperparameters on inverse problem accuracy. We further design a meteorology-adaptive training strategy and incorporate strong PDE constraints to enhance physical consistency. The resulting robust configuration is validated end-to-end across diverse synthetic scenarios and real-world atmospheric variability datasets. Our approach significantly improves source localization accuracy and monitoring system stability, particularly under high uncertainty. It establishes a generalizable methodological framework for solving ill-posed inverse problems in environmental sensing, advancing the reliability of data-driven atmospheric inversion under realistic, non-ideal conditions.

Technology Category

• Search and Optimization: Adversarial Search
• Multiagent Systems: Adversarial Agents
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper investigates the application of Physics-Informed Neural Networks (PINNs) for solving the inverse advection-diffusion problem to localize pollution sources. The study focuses on optimizing neural network architectures to accurately model pollutant dispersion dynamics under diverse conditions, including scenarios with weak and strong winds and multiple pollution sources. Various PINN configurations are evaluated, showing the strong dependence of solution accuracy on hyperparameter selection. Recommendations for efficient PINN configurations are provided based on these comparisons. The approach is tested across multiple scenarios and validated using real-world data that accounts for atmospheric variability. The results demonstrate that the proposed methodology achieves high accuracy in source localization, showcasing the stability and potential of PINNs for addressing environmental monitoring and pollution management challenges under complex weather conditions.

Problem

• Localizing pollution sources using inverse advection-diffusion modeling
• Optimizing neural networks for accurate pollutant dispersion dynamics
• Validating PINN accuracy in complex weather conditions

Innovation

• Physics-Informed Neural Networks for pollution localization
• Optimized neural network architectures for pollutant dispersion
• Hyperparameter-dependent accuracy in source localization