Physics-Informed Neural Networks for Source Inversion and Parameters Estimation in Atmospheric Dispersion

ArXi:2512.07755v2 Announce Type: replace-cross Recent studies have shown the success of deep learning in solving forward and inverse problems in engineering and scientific computing domains, such as physics-informed neural networks (PINNs). In the fields of atmospheric science and environmental monitoring, estimating emission source locations is a central task that further relies on multiple model parameters that dictate velocity profiles and diffusion parameters. Estimating these parameters at the same time as emission sources from scarce data is a difficult task.