# Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls

arXiv: https://arxiv.org/abs/2304.12420 · HTML: https://mlsanity.com/paper/2304.12420/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 81 references that are in the map.
- First posted on arXiv 24 Apr 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks](https://mlsanity.com/paper/1905.13166/) (2019)
- [Direct shape optimization through deep reinforcement learning](https://mlsanity.com/paper/1908.09885/) (2019)
- [U-net architectures for fast prediction of incompressible laminar flows](https://mlsanity.com/paper/1910.13532/) (2019)
- [Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression](https://mlsanity.com/paper/2211.10360/) (2022)
- [Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design](https://mlsanity.com/paper/2302.14732/) (2023)
- [Search for universal minimum drag resistance underwater vehicle hull using CFD](https://mlsanity.com/paper/2302.09441/) (2023)
- [Shape optimization in laminar flow with a label-guided variational autoencoder](https://mlsanity.com/paper/1712.03599/) (2017)

## Built on by (most cited first, 4 of 4)

- [Generative AI for CAD Automation: Leveraging Large Language Models for 3D Modelling](https://mlsanity.com/paper/2508.00843/) (2025)
- [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)

Physics simulations like computational fluid dynamics (CFD) are a computational bottleneck in computer-aided design (CAD) optimization processes. To overcome this bottleneck, one requires either an optimization framework that is highly sample-efficient, or a fast data-driven proxy (surrogate model) for long-running simulations. Both approaches have benefits and limitations. Bayesian optimization is often used for sample efficiency, but it solves one specific problem and struggles with transferability; alternatively, surrogate models can offer fast and often more generalizable solutions for CFD problems, but gathering data for and training such models can be computationally demanding. 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. Our study finds that the Bayesian Optimization-Lower Condition Bound (BO-LCB) algorithm is the most sample-efficient optimization framework and has the best convergence behavior of those considered. Subsequently, we show that our DNN-based surrogate model predicts drag force on test data in tight agreement with CFD simulations, with a mean absolute percentage error (MAPE) of 1.85%. Combining these results, we demonstrate a two-orders-of-magnitude speedup (with comparable accuracy) for the design optimization process when the surrogate model is used. To our knowledge, this is the first study applying Bayesian optimization and DNN-based surrogate modeling to the problem of UUV design optimization, and we share our developments as open-source software.