Accurate modeling of vacuum dynamics in Trailing Suction Hopper Dredgers (TSHDs) is critical for optimizing suction production and mitigating sensor anomalies. This study proposes a data-driven, physics-guided operator learning framework to estimate the vacuum pressure loss parameter θ, a variable derived from physical principles in dredging operations. Leveraging a modified Deep Operator Network (DeepONet), we introduce attention-based interactions between branches and the trunk network to capture complex dependencies in the sensor data. A local trunk mechanism is introduced to preserve temporal locality across dredging trips.
Due to the nature of a lagging density sensor, we integrate a real-time rolling mean error correction mechanism. This addresses training biases for refined predictions, as well as offering an anomaly detection mechanism. The model is trained and validated on real-world vessel data, including synthetic simulations of vacuum processes, and evaluated using trip-wise and global metrics. Experimental results show that the proposed architecture significantly outperforms the rolling mean baseline setups and the classical DeepONet across accuracy metrics such as the root mean square error (RMSE).
This work demonstrates the value of combining domain knowledge with operator learning techniques in maritime engineering. The proposed framework offers a scalable framework, allowing application across entire fleets for real-time suction production estimation and anomaly detection, contributing to efficient dredging operations.

The ability to accurately forecast sales volumes holds substantial significance for businesses. Current classical models struggle in capturing the impact of different variables upon the sales volume. These machine learning models are also not applicable to more than one specific product. The Temporal Fusion Transformer (TFT) is implemented to address these issues. The TFT is a powerful tool designed for time series forecasting. TFT leverages deep neural networks and self-attention to capture variable dependencies across all time steps,providing temporal context for improved accuracy. The developed TFT model showcases its efficacy in accurate
sales forecasting. By considering both past and future variables, TFT generates predictions with errors of 30%. Moreover, the interpretability of the model highlights the importance of variables such as Covid lockdown periods and product distribution. The scalability of the TFT model allows it to generate forecasts for every product-retailer combination, making it a versatile tool for businesses. As a multi-horizon forecaster, TFT incorporates both past and future variables to generate predictions. This characteristic makes it an excellent candidate for evaluating the impact of changes in future inputs controlled by the business, such as pricing and distribution strategies.