Collocation-based Robust Physics Informed Neural Networks for time-dependent simulations of pollution propagation under thermal inversion conditions on Spitsbergen
arXiv cs.LG / 4/28/2026
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Key Points
- The paper introduces a Physics-Informed Neural Network (PINN) framework to simulate time-dependent pollution transport from moving emission sources using a robust variational formulation for the advection–diffusion equation.
- It proves mathematical properties for the discrete weak formulation—boundedness and inf-sup stability—and uses this basis to design a robust loss function linked to the true approximation error.
- To improve training efficiency, the authors add a collocation-based strategy that accelerates neural network training without relying on the exact solution.
- As a real-world case study, the method is applied to pollution from snowmobile traffic in Longyearbyen, Spitsbergen, using in-field sensor measurements, to quantify how thermal inversion increases near-ground pollutant accumulation.
- The results indicate that thermal inversion traps dense, humid air close to the ground, substantially raising particulate matter (PM) concentrations and degrading local air quality.
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