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\begin{document}

\title{Forecasting Buoy Observations Using Physics-Informed Neural Networks}



\author[1]{Austin B. Schmidt}%
\author[1]{Pujan Pokhrel}%
\author[1]{Mahdi Abdelguerfi}%
\author[1]{Elias Ioup}%
\author[1]{David Dobson}%
\affil[1]{Affiliation not available}%


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  \date{June 13, 2024}


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\begin{abstract}
Methodologies inspired by physics-informed neural networks (PINNs) were
used to forecast observations recorded by stationary ocean buoys. We
combined buoy observations with numerical models to train surrogate deep
learning networks that performed better than with either data alone.
Numerical model outputs were collected from two sources for training and
regularization: the hybrid circulation ocean model and the fifth ECMWF
reanalysis experiment. A hyperparameter determines the ratio of
observational and modeled data to be used in the training procedure, so
we conducted a grid search to find the most performant ratio. Overall,~
the technique improved the general forecast performance compared with
nonregularized models. Under specific circumstances, the regularization
mechanism enabled the PINN models to be more accurate than the numerical
models. This demonstrates the utility of combining various climate
models and sensor observations to improve surrogate modeling.%
\end{abstract}%



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