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Found 9 papers, 4 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.
Search for universal minimum drag resistance underwater vehicle hull using CFD
However, a design that is optimal at high velocity and high turbulence conditions performs near-optimal across many considered velocity and turbulence conditions.
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.
Deep Active Learning for Regression Using $ε$-weighted Hybrid Query Strategy
However, the main challenge in creating a DL-based surrogate is to simulate/label a large number of design points, which is time-consuming for computationally costly and/or high-dimensional engineering problems.
Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models
In this paper, we introduce a novel approach for this detection process using a Reduced Robust Random Cut Forest (RRRCF) data structure, which can be used on both small and large data sets.
Rare event failure test case generation in Learning-Enabled-Controllers
Finding a good test case that can reveal the potential failure in these trained systems can help to retrain these models to increase their correctness.
Deep Learning-based Finite Element Analysis (FEA) surrogate for sub-sea pressure vessel
Once the surrogate is trained for a class of problem, then the learned response surface can be used to analyze the stress effect without running the FEA for that class of problem.
