## 6 Conclusions and Future Perspectives
### Directions for Future Research
#### 6.3.5 Towards Physic-Informed Neural Networks (PINNs)  
While conventional data-driven DL models have demonstrated strong predictive performance, they often lack physical consistency and interpretability. PINNs offer a promising research direction by integrating physical laws into the learning process. Instead of optimizing solely for empirical loss, PINNs incorporate physics-based information, such as constraints derived from differential equations, into the loss function, thereby guiding the model to learn representations that are both data-driven and physically consistent. Future research could apply PINNs in ocean and climate research to enhance robustness and generalizability in data-sparse regions or under extreme conditions. For example, in modeling coastal sea level rise, PINNs can incorporate governing equations from fluid dynamics and tidal forcing, ensuring that predicted sea level variations not only fit observations but also adhere to physical constraints. In GNSS-R-based sea surface wind speed estimation, PINNs can integrate geophysical models, such as the relationship between normalized bistatic radar cross-section and wind speed, into the training process, ensuring the predicted wind speeds are consistent with electromagnetic scattering physics. These physics-informed models are beneficial for mitigating overfitting and producing more physically consistent outputs across space and time, especially valuable for complex scenarios where purely data-driven models may capture spurious correlations that violate known physical laws. By embedding prior knowledge and domain constraints, PINNs enhance the practical reliability of DL models in oceanography and climate science.  
Moreover, new data sources such as NASA-ISRO Synthetic Aperture Radar [340] and ESA HydroGNSS [341] will offer unprecedented observations of key ocean parameters. At the same time, emerging technologies such as Large Language Models (LLMs) and quantum computing present exciting opportunities to further revolutionize ocean climate research [342; 343]. LLMs, trained on vast and diverse datasets, can be fine-tuned to address a wide range of ocean climate change tasks. Quantum computing, with its exceptional computational power, presents the ability to tackle complex challenges such as simulating large-scale ocean circulation. The integration of these innovative technologies with existing DL frameworks could open new frontiers in ocean climate change research.  
Conceptualization, X.Q. and W.H.; methodology, X.Q., K.Z. and W.H.; investigation, X.Q., K.Z. and W.H.; writing--original draft preparation, X.Q., K.Z. and W.H.; writing--review and editing, X.Q. and W.H.; visualization, X.Q., K.Z. and W.H.; supervision, W.H. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grants under Grant NSERC RGPIN-2024-04442.  
No new data were created or analyzed in this study.  
The authors would like to express gratitude to the icon creators from Flaticon.  
The authors declare that there are no conflicts of interest.