A Neuro-Symbolic Approach for Marine Vessels Power Prediction Under Distribution Shifts
2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)(2023)
Abstract
This paper proposes a neuro-symbolic approach to predict the power of marine cargo vessels. The neuro-symbolic approach combines two parts. The first is a neural networks part, and the second is a symbolic part that relies on physics-based formulae. The Shifts-power dataset was used for evaluation. The experimental results showed that a combination of a physics-based module (symbolic part) with a neural networks model (namely ensemble Monte Carlo dropout) superseded the state-of-the-art results by 2.3% in terms of uncertainty estimation measured using R-AUC, and by 3.4% in terms of power prediction for out-of-distribution (OOD) examples measured using RMSE. It also superseded the symbolic approach by 6.3% in terms of uncertainty and 17.7% in terms of OOD power prediction.
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Key words
deep learning,distribution shifts,neuro-symbolic,uncertainty,marine vessels,power prediction
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