Collocation-based Robust Physics Informed Neural Networks for time-dependent simulations of pollution propagation under thermal inversion conditions on Spitsbergen
Leszek Siwik, Maciej Sikora, Natalia Leszczyńska, Tomasz Maciej Ciesielski, Eirik Valseth + 5 more
TLDR
This paper introduces a robust, collocation-based PINN for time-dependent pollution simulation, revealing how thermal inversion increases PM.
Key contributions
- Proposes a robust PINN framework for time-dependent pollution from moving emission sources.
- Develops a stable variational framework and a loss function tied to true approximation error.
- Integrates a collocation strategy to significantly accelerate neural network training.
- Demonstrates how thermal inversion drastically increases particulate matter concentration in Spitsbergen.
Why it matters
This paper offers a robust and efficient PINN approach for modeling complex environmental pollution. Its findings on thermal inversion's impact on air quality, validated with real-world data, are crucial for environmental policy and urban planning in vulnerable regions.
Original Abstract
In this paper, we propose a Physics-Informed Neural Network framework for time-dependent simulations of pollution propagation originating from moving emission sources. We formulate a robust variational framework for the time-dependent advection-diffusion problem and establish the boundedness and inf-sup stability of the corresponding discrete weak formulation. Based on this mathematical foundation, we construct a robust loss function that is directly related to the true approximation error, defined as the difference between the neural network approximation and the (unknown) exact solution. Additionally, a collocation-based strategy is introduced to speed up neural network training. As a case study, we investigate pollution propagation caused by snowmobile traffic in Longyearbyen, Spitsbergen, supported by detailed in-field measurements collected using dedicated sensors. The proposed framework is applied to analyze the effects of thermal inversion on pollutant accumulation. Our results demonstrate that thermal inversion traps dense and humid air masses near the ground, significantly enhancing particulate matter (PM) concentration and worsening local air quality.
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