Technology Category
Search and Optimization: Adversarial SearchMultiagent Systems: Adversarial AgentsMachine Learning: Adversarial Learning & Robustness
Application of Physics-Informed Neural Networks for Solving the Inverse Advection-Diffusion Problem to Localize Pollution Sources
🤖 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
Application Category
📝 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
Research questions and friction points this paper is trying to address.
Localizing pollution sources using inverse advection-diffusion modeling
Optimizing neural networks for accurate pollutant dispersion dynamics
Validating PINN accuracy in complex weather conditions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Physics-Informed Neural Networks for pollution localization
Optimized neural network architectures for pollutant dispersion
Hyperparameter-dependent accuracy in source localization
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