Predicting Ocean Variables Using PINNs And Transformer Architecture

Abstract

The prediction of ocean variables, such as temperature and velocity, poses significant challenges due to the complex and dynamic nature of the ocean. The prediction models face limitations and uncertainties, stemming from the nonlinear interactions of oceanic processes. Seawater temperature, in particular, plays a crucial role in marine ecosystems and global climate dynamics, underscoring the importance of accurately predicting it. Our study aims to explore the efficacy of physics-informed neural networks, and leveraging a Transformer based architecture combined with convolutional neural networks, for predicting sea surface temperature using short-wave radiation data. It demonstrates the promise of transformer based models for ocean variable prediction, with ongoing efforts aimed at refining model architecture and training strategies to achieve more robust and accurate predictions. How ever, challenges persist in optimizing model performance. Further exploration is needed to enhance model reliability and reduce prediction errors, potentially by incorporating additional variables and exploring alternative training mechanisms