Physical Informed Neural Networks for modeling ocean pollutant

Karishma Battina, Prathamesh Dinesh Joshi, Raj Dandekar, Rajat Dandekar, Sreedath Panat

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 advectiondiffusion 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 Julia’s scientific computing ecosystem for high-performance simulations,
offering a scalable and flexible alternative to traditional solvers.