A Hybrid Framework for Airfoil Optimization: Combining PINNs and Genetic Algorithm (Student Abstract)

Shubhanshu Rao, Gaurav Kumar, Martin Agelin-Chaab

AAAI 2025 pp. 29475-29476

doi:10.1609/AAAI.V39I28.35293 /aaai/2025/rao2025aaai-hybrid/

Abstract

Achieving optimal design is a crucial aspect of any design process for safe and efficient operation. Such tasks typically require numerous simulations over many iterations, which can become computationally expensive. This paper proposes a novel method that combines Physics-informed Neural Networks (PINNs) with a Genetic Algorithm to optimize the parameters of an airfoil that aims to achieve favourable aerodynamic conditions. Traditional solvers are computationally expensive for performing such tasks, but using PINNs can significantly reduce this while keeping accuracy high. The proposed approach shows the advantage of using PINNs in optimizing complex engineering problems.

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Cite

Text

Rao et al. "A Hybrid Framework for Airfoil Optimization: Combining PINNs and Genetic Algorithm (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35293

Markdown

[Rao et al. "A Hybrid Framework for Airfoil Optimization: Combining PINNs and Genetic Algorithm (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/rao2025aaai-hybrid/) doi:10.1609/AAAI.V39I28.35293

BibTeX

@inproceedings{rao2025aaai-hybrid,
  title     = {{A Hybrid Framework for Airfoil Optimization: Combining PINNs and Genetic Algorithm (Student Abstract)}},
  author    = {Rao, Shubhanshu and Kumar, Gaurav and Agelin-Chaab, Martin},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29475-29476},
  doi       = {10.1609/AAAI.V39I28.35293},
  url       = {https://mlanthology.org/aaai/2025/rao2025aaai-hybrid/}
}