Physical Informed Neural Networks for modeling ocean pollutant
This provides a scalable and flexible alternative to traditional numerical methods for ocean pollutant modeling, addressing challenges like non-linear dynamics and boundary conditions, but it is incremental as it applies an existing PINN method to a specific domain.
The paper tackles modeling ocean pollutant transport by introducing a Physics-Informed Neural Network (PINN) framework that simulates pollutant dispersion using the 2D advection-diffusion equation, achieving physically consistent predictions by embedding physical laws and fitting to noisy synthetic data.
Traditional numerical methods often struggle with the complexity and scale of modeling pollutant transport across vast and dynamic oceanic domains. This paper introduces a Physics-Informed Neural Network (PINN) framework to simulate the dispersion of pollutants governed by the 2D advection-diffusion equation. The model achieves physically consistent predictions by embedding physical laws and fitting to noisy synthetic data, generated via a finite difference method (FDM), directly into the neural network training process. This approach addresses challenges such as non-linear dynamics and the enforcement of boundary and initial conditions. Synthetic data sets, augmented with varying noise levels, are used to capture real-world variability. The training incorporates a hybrid loss function including PDE residuals, boundary/initial condition conformity, and a weighted data fit term. The approach takes advantage of the Julia language scientific computing ecosystem for high-performance simulations, offering a scalable and flexible alternative to traditional solvers