Physical Informed Neural Networks for modeling ocean pollutant

TL;DR

This paper presents a Physics-Informed Neural Network framework for modeling ocean pollutant dispersion, integrating physical laws with data to improve prediction accuracy in complex, large-scale oceanic environments.

Contribution

It introduces a novel PINN approach for 2D advection-diffusion equations, combining physics-based constraints with noisy synthetic data for scalable ocean pollutant modeling.

Findings

Achieves physically consistent pollutant dispersion predictions.

Effectively handles noisy data and complex boundary conditions.

Demonstrates scalability using Julia language ecosystem.

Abstract

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