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Forecasting Buoy Observations Using Physics-Informed Neural Networks
  • +2
  • Austin B. Schmidt,
  • Pujan Pokhrel,
  • Mahdi Abdelguerfi,
  • Elias Ioup,
  • David Dobson
Austin B. Schmidt

Corresponding Author:[email protected]

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Pujan Pokhrel
Mahdi Abdelguerfi
Elias Ioup
David Dobson


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.
16 Apr 2024Submitted to TechRxiv
18 Apr 2024Published in TechRxiv
2024Published in IEEE Journal of Oceanic Engineering on pages 1-20. 10.1109/JOE.2024.3378408