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Found 5 papers, 1 papers with code
Computationally Efficient Optimisation of Elbow-Type Draft Tube Using Neural Network Surrogates
The success history-based adaptive differential evolution with linear reduction and the multi-objective evolutionary algorithm based on decomposition were identified as the best-performing algorithms and used to determine the influence of different objectives in the single-objective optimisation and their combined impact on the draft tube design in the multi-objective optimisation.
Reconstruction and analysis of negatively buoyant jets with interpretable machine learning
In order to understand the working of the machine learning model and the influence of all parameters on the geometrical characteristics of inclined buoyant jets, the SHAP feature interpretation method was used.
Machine learning based surrogate models for microchannel heat sink optimization
Overall, we have demonstrated that the proposed framework has merit and can be used as a viable methodology in microchannel heat sink design optimization.
Predictive modeling of microbiological seawater quality classification in karst region using cascade model
Currently, the cascade model is employed as a filter method, where measurements not classified as excellent quality need to be further analyzed.
Coastal water quality prediction based on machine learning with feature interpretation and spatio-temporal analysis
Finally, the spatial and temporal accuracy of both ML models were examined at sites with the lowest coastal water quality.
