mlsanityData to 14 Oct 2026

Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient

arXiv 1712.10082 ↗First posted 29 Dec 2017stat.ML cs.LGcited by 26

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 3; 2023: 7; 2024: 3; 2025: 2; 2026: 2.

26 papers.

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  1. 2021Simulating Continuum Mechanics with Multi-Scale Graph Neural Networkscited by 40
  2. 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
  3. 2021Constraint-based graph network simulatorcited by 27
  4. 2023Weak-Form Latent Space Dynamics Identificationcited by 20
  5. 2023Swarm Reinforcement Learning For Adaptive Mesh Refinementcited by 17
  6. 2021Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regressioncited by 15
  7. 2023Grounding Graph Network Simulators using Physical Sensor Observationscited by 15
  8. 2021Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolutioncited by 14
  9. 2024A review of graph neural network applications in mechanics-related domainscited by 14
  10. 2021Predicting Mechanically Driven Full-Field Quantities of Interest with Deep Learning-Based Metamodelscited by 10
  11. 2021Surrogate Modeling of Fluid Dynamics with a Multigrid Inspired Neural Network Architecturecited by 9
  12. 2021Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimizationcited by 7
14 more
  1. 2023Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Designcited by 5
  2. 2022An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networkscited by 4
  3. 2022A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Methodcited by 3
  4. 2024Learning Physical Simulation with Message Passing Transformercited by 3
  5. 20243DMeshNet: A Three-Dimensional Differential Neural Network for Structured Mesh Generationcited by 3
  6. 2021Airfoil's Aerodynamic Coefficients Prediction using Artificial Neural Networkcited by 1
  7. 2023Knowledge-embedded meta-learning model for lift coefficient prediction of airfoilscited by 1
  8. 2025Accelerated Airfoil Design Using Neural Network Approachescited by 1
  9. 2025Adaptive Graph Rewiring to Mitigate Over-Squashing in Mesh-Based GNNs for Fluid Dynamics Simulationscited by 1
  10. 2026A panoramic aerodynamic performance prediction method for turbomachinery cascades using transformer-enhanced neural operatorcited by 1
  11. 2021An FEA surrogate model with Boundary Oriented Graph Embedding approachcited by 0
  12. 2023SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamicscited by 0
  13. 2023Latent Task-Specific Graph Network Simulatorscited by 0
  14. 2026VATO: A Vortex-Force-Aware Transformer Operator for Unsteady Separated Aerofoil Flowscited by 0