# Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient

arXiv: https://arxiv.org/abs/1712.10082 · HTML: https://mlsanity.com/paper/1712.10082/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2017.

## Built on by (most cited first, 26 of 26)

- [Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks](https://mlsanity.com/paper/2106.04900/) (2021)
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- [Predicting Mechanically Driven Full-Field Quantities of Interest with Deep Learning-Based Metamodels](https://mlsanity.com/paper/2108.03995/) (2021)
- [Surrogate Modeling of Fluid Dynamics with a Multigrid Inspired Neural Network Architecture](https://mlsanity.com/paper/2105.03854/) (2021)
- [Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimization](https://mlsanity.com/paper/2101.04757/) (2021)
- [Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design](https://mlsanity.com/paper/2302.00557/) (2023)
- [An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networks](https://mlsanity.com/paper/2210.09546/) (2022)
- [A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Method](https://mlsanity.com/paper/2206.12254/) (2022)
- [Learning Physical Simulation with Message Passing Transformer](https://mlsanity.com/paper/2406.06060/) (2024)
- [3DMeshNet: A Three-Dimensional Differential Neural Network for Structured Mesh Generation](https://mlsanity.com/paper/2407.01560/) (2024)
- [Airfoil's Aerodynamic Coefficients Prediction using Artificial Neural Network](https://mlsanity.com/paper/2109.12149/) (2021)
- [Knowledge-embedded meta-learning model for lift coefficient prediction of airfoils](https://mlsanity.com/paper/2303.02844/) (2023)
- [Accelerated Airfoil Design Using Neural Network Approaches](https://mlsanity.com/paper/2503.24052/) (2025)
- [Adaptive Graph Rewiring to Mitigate Over-Squashing in Mesh-Based GNNs for Fluid Dynamics Simulations](https://mlsanity.com/paper/2511.12709/) (2025)
- [A panoramic aerodynamic performance prediction method for turbomachinery cascades using transformer-enhanced neural operator](https://mlsanity.com/paper/2609.16066/) (2026)
- [An FEA surrogate model with Boundary Oriented Graph Embedding approach](https://mlsanity.com/paper/2108.13509/) (2021)
- [SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics](https://mlsanity.com/paper/2310.20049/) (2023)
- [Latent Task-Specific Graph Network Simulators](https://mlsanity.com/paper/2311.05256/) (2023)
- [VATO: A Vortex-Force-Aware Transformer Operator for Unsteady Separated Aerofoil Flows](https://mlsanity.com/paper/2609.00507/) (2026)

## Abstract (arXiv)

The adaptability of the convolutional neural network (CNN) technique for aerodynamic meta-modeling tasks is probed in this work. The primary objective is to develop suitable CNN architecture for variable flow conditions and object geometry, in addition to identifying a sufficient data preparation process. Multiple CNN structures were trained to learn the lift coefficients of the airfoils with a variety of shapes in multiple flow Mach numbers, Reynolds numbers, and diverse angles of attack. This is conducted to illustrate the concept of the technique. A multi-layered perceptron (MLP) is also used for the training sets. The MLP results are compared with that of the CNN results. The newly proposed meta-modeling concept has been found to be comparable with the MLP in learning capability; and more importantly, our CNN model exhibits a competitive prediction accuracy with minimal constraints in a geometric representation.