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Found 4 papers, 2 papers with code
Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls
In this work, we leverage recent advances in optimization and artificial intelligence (AI) to explore both of these potential approaches, in the context of designing an optimal unmanned underwater vehicle (UUV) hull.
Fusion of ML with numerical simulation for optimized propeller design
In this work, we propose an alternative way to use ML model to surrogate the design process that formulates the search problem as an inverse problem and can save time by finding the optimal design or at least a good initial seed design for optimization.
Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design
Second, it needs integration of a sample efficient optimization framework with the integrated toolchain.
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression
In a computer-aided engineering design optimization problem that involves notoriously complex and time-consuming simulator, the prevalent approach is to replace these simulations with a data-driven surrogate that approximates the simulator's behavior at a much cheaper cost.
