# Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design

arXiv: https://arxiv.org/abs/2302.14732 · HTML: https://mlsanity.com/paper/2302.14732/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [Rare event failure test case generation in Learning-Enabled-Controllers](https://mlsanity.com/paper/2206.05533/) (2022)
- [Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression](https://mlsanity.com/paper/2211.10360/) (2022)
- [Search for universal minimum drag resistance underwater vehicle hull using CFD](https://mlsanity.com/paper/2302.09441/) (2023)
- [Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models](https://mlsanity.com/paper/2206.09247/) (2022)
- [Shape optimization in laminar flow with a label-guided variational autoencoder](https://mlsanity.com/paper/1712.03599/) (2017)

## Built on by (most cited first, 9 of 9)

- [Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls](https://mlsanity.com/paper/2304.12420/) (2023)
- [Generative AI for CAD Automation: Leveraging Large Language Models for 3D Modelling](https://mlsanity.com/paper/2508.00843/) (2025)
- [Vehicle lateral control using Machine Learning for automated vehicle guidance](https://mlsanity.com/paper/2303.08187/) (2023)
- [Automatic AI controller that can drive with confidence: steering vehicle with uncertainty knowledge](https://mlsanity.com/paper/2404.16893/) (2024)
- [Systematic design space exploration by learning the explored space using Machine Learning](https://mlsanity.com/paper/2303.08249/) (2023)
- [Optimization for truss design using Bayesian optimization](https://mlsanity.com/paper/2306.01763/) (2023)
- [Anvil: An integration of artificial intelligence, sampling techniques, and a combined CAD-CFD tool](https://mlsanity.com/paper/2407.02519/) (2024)
- [AI-Enhanced Automatic Design of Efficient Underwater Gliders](https://mlsanity.com/paper/2505.00222/) (2025)
- [Efficient Sliced Wasserstein Distance Computation via Adaptive Bayesian Optimization](https://mlsanity.com/paper/2509.17405/) (2025)

## Abstract (arXiv)

Automatic underwater vehicle hull Design optimization is a complex engineering process for generating a UUV hull with optimized properties on a given requirement. First, it involves the integration of involved computationally complex engineering simulation tools. Second, it needs integration of a sample efficient optimization framework with the integrated toolchain. To this end, we integrated the CAD tool called FreeCAD with CFD tool openFoam for automatic design evaluation. For optimization, we chose Bayesian optimization (BO), which is a well-known technique developed for optimizing time-consuming expensive engineering simulations and has proven to be very sample efficient in a variety of problems, including hyper-parameter tuning and experimental design. During the optimization process, we can handle infeasible design as constraints integrated into the optimization process. By integrating domain-specific toolchain with AI-based optimization, we executed the automatic design optimization of underwater vehicle hull design. For empirical evaluation, we took two different use cases of real-world underwater vehicle design to validate the execution of our tool.