# Shape optimization in laminar flow with a label-guided variational autoencoder

arXiv: https://arxiv.org/abs/1712.03599 · HTML: https://mlsanity.com/paper/1712.03599/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2017.

## Built on by (most cited first, 5 of 5)

- [Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design](https://mlsanity.com/paper/2302.14732/) (2023)
- [Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls](https://mlsanity.com/paper/2304.12420/) (2023)
- [A machine learning approach for fighting the curse of dimensionality in global optimization](https://mlsanity.com/paper/2110.14985/) (2021)
- [Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs](https://mlsanity.com/paper/2112.01221/) (2021)
- [Heat Conduction Plate Layout Optimization using Physics-driven Convolutional Neural Networks](https://mlsanity.com/paper/2201.10002/) (2022)

## Abstract (arXiv)

Computational design optimization in fluid dynamics usually requires to solve non-linear partial differential equations numerically. In this work, we explore a Bayesian optimization approach to minimize an object's drag coefficient in laminar flow based on predicting drag directly from the object shape. Jointly training an architecture combining a variational autoencoder mapping shapes to latent representations and Gaussian process regression allows us to generate improved shapes in the two dimensional case we consider.