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Explainable AI could be the key to unlocking transparency and better decision-making in ship fuel optimization, but data quality and real-time optimization remain significant hurdles.
By rediscovering known physics like cubic speed-power relationships, PI-KAN shows that hybrid models can achieve both accuracy and interpretability in vessel performance prediction, outperforming black-box neural nets.
