# Airfoil optimization using Design-by-Morphing with minimized design-space dimensionality

arXiv: https://arxiv.org/abs/2510.16020 · HTML: https://mlsanity.com/paper/2510.16020/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [Machine Learning in Aerodynamic Shape Optimization](https://mlsanity.com/paper/2202.07141/) (2022)
- [Airfoil Design Parameterization and Optimization using B'ezier Generative Adversarial Networks](https://mlsanity.com/paper/2006.12496/) (2020)
- [The Hypervolume Indicator: Problems and Algorithms](https://mlsanity.com/paper/2005.00515/) (2020)
- [Parametric Generative Schemes with Geometric Constraints for Encoding and Synthesizing Airfoils](https://mlsanity.com/paper/2205.02458/) (2022)
- [Optimization of the Shape of a Hydrokinetic Turbine's Draft Tube and Hub Assembly Using Design-by-Morphing with Bayesian Optimization](https://mlsanity.com/paper/2207.11451/) (2022)
- [Enhancing Vehicle Aerodynamics with Deep Reinforcement Learning in Voxelised Models](https://mlsanity.com/paper/2405.11492/) (2024)
- [A Survey on Design-space Dimensionality Reduction Methods for Shape Optimization](https://mlsanity.com/paper/2405.13944/) (2024)
- [A systematic dataset generation technique applied to data-driven automotive aerodynamics](https://mlsanity.com/paper/2408.07318/) (2024)
- [Airfoil Optimization using Design-by-Morphing](https://mlsanity.com/paper/2207.11448/) (2022)

## Abstract (arXiv)

Effective airfoil geometry optimization requires exploring a diverse range of designs using as few design variables as possible. This study introduces AirDbM, a Design-by-Morphing (DbM) approach specialized for airfoil optimization that systematically reduces design-space dimensionality. AirDbM selects an optimal set of 12 baseline airfoils from the UIUC airfoil database, which contains over 1,600 shapes, by sequentially adding the baseline that most increases the design capacity. With these baselines, AirDbM reconstructs 99 % of the database with a mean absolute error below 0.005, which matches the performance of a previous DbM approach that used more baselines. In multi-objective aerodynamic optimization, AirDbM demonstrates rapid convergence and achieves a Pareto front with a greater hypervolume than that of the previous larger-baseline study, where new Pareto-optimal solutions are discovered with enhanced lift-to-drag ratios at moderate stall tolerances. Furthermore, AirDbM demonstrates outstanding adaptability for reinforcement learning (RL) agents in generating airfoil geometry when compared to conventional airfoil parameterization methods, implying the broader potential of DbM in machine learning-driven design.