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Fast Predictions of Aircraft Aerodynamics using Deep Learning Techniques

https://doi.org/10.2514/6.2021-2549
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00466-019-01740-0openalex-snapshot
Prediction of aerodynamic flow fields using convolutional neural networks
inferredcites →https://doi.org/10.1007/s13272-018-0283-6openalex-snapshot
Reduced-order models for aerodynamic applications, loads and MDO
inferredcites →https://doi.org/10.1016/j.ast.2016.12.029openalex-snapshot
Reduced order unsteady aerodynamic model of a rigid aerofoil in gust encounters
inferredcites →https://doi.org/10.1016/j.compfluid.2019.104393openalex-snapshot
A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils
inferredcites →https://doi.org/10.1016/j.jfluidstructs.2012.12.007openalex-snapshot
Gust response: Simulation of an aeroelastic experiment by a fluid–structure interaction method
inferredcites →https://doi.org/10.1016/j.jfluidstructs.2014.04.014openalex-snapshot
Computational aeroelastic investigation of a transonic limit-cycle-oscillation experiment at a transport aircraft wing model
inferredcites →https://doi.org/10.1016/j.paerosci.2003.12.001openalex-snapshot
Reduced-order modeling: new approaches for computational physics
inferredcites →https://doi.org/10.1016/j.paerosci.2008.11.001openalex-snapshot
Recent advances in surrogate-based optimization
inferredcites →https://doi.org/10.1016/j.procs.2010.04.019openalex-snapshot
Non-linear reduced order models for steady aerodynamics
inferredcites →https://doi.org/10.1017/aer.2017.104openalex-snapshot
Rapid gust response simulation of large civil aircraft using computational fluid dynamics
inferredcites →https://doi.org/10.1023/a:1012771025575openalex-snapshot
A Taxonomy of Global Optimization Methods Based on Response Surfaces
inferredcites →https://doi.org/10.1063/1.5024595openalex-snapshot
Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder
inferredcites →https://doi.org/10.1063/1.5094943openalex-snapshot
Fast flow field prediction over airfoils using deep learning approach
inferredcites →https://doi.org/10.1090/qam/910462openalex-snapshot
Turbulence and the dynamics of coherent structures. I. Coherent structures
inferredcites →https://doi.org/10.1137/090766498openalex-snapshot
Nonlinear Model Reduction via Discrete Empirical Interpolation
inferredcites →https://doi.org/10.1145/2939672.2939738openalex-snapshot
Convolutional Neural Networks for Steady Flow Approximation
inferredcites →https://doi.org/10.1145/355588.365104openalex-snapshot
Algorithm 247: Radical-inverse quasi-random point sequence
inferredcites →https://doi.org/10.1155/2010/494070openalex-snapshot
Asymptotic Behavior of the Likelihood Function of Covariance Matrices of Spatial Gaussian Processes
inferredcites →https://doi.org/10.1214/ss/1177012413openalex-snapshot
Design and Analysis of Computer Experiments
inferredcites →https://doi.org/10.1561/2200000006openalex-snapshot
Learning Deep Architectures for AI
inferredcites →https://doi.org/10.2514/1.j053755openalex-snapshot
Evaluation of Aerodynamic Loads via Reduced-Order Methodology
inferredcites →https://doi.org/10.2514/1.j057804openalex-snapshot
Nonlinear Unsteady Reduced-Order Modeling for Gust-Load Predictions
inferredcites →https://doi.org/10.2514/1.j058291openalex-snapshot
Deep Learning Methods for Reynolds-Averaged Navier–Stokes Simulations of Airfoil Flows
inferredcites →https://doi.org/10.2514/6.2003-4213openalex-snapshot
Proper Orthogonal Decomposition Extensions for Parametric Applications in Compressible Aerodynamics
inferredcites →https://doi.org/10.2514/6.2004-2329openalex-snapshot
Reduced-Order, Trajectory Piecewise-Linear Models for Nonlinear Computational Fluid Dynamics
inferredcites →https://doi.org/10.2514/6.2008-887openalex-snapshot
Aerodynamic Coefficient Prediction of Airfoils Using Neural Networks
inferredcites →https://doi.org/10.2514/6.2017-3660openalex-snapshot
A Convolutional Neural Network Approach to Training Predictors for Airfoil Performance

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4820053openalex-extracted
Predicting Transonic Flowfields in Non-Homogeneous Unstructured Grids Using Autoencoder Graph Convolutional Networks
inferred← citeshttps://doi.org/10.1007/978-981-96-4652-4_65openalex-snapshot
Digital Twin Modeling of Hypersonic Flight Vehicles Based on Dynamic Regression Neural Network
inferred← citeshttps://doi.org/10.1007/s42423-025-00172-8openalex-snapshot
A Data-Driven Equivalent Modeling Approach for Large-Amplitude Liquid Sloshing Under Microgravity Environment
inferred← citeshttps://doi.org/10.1016/j.ast.2026.112743openalex-snapshot
Fusing CFD and measurement data using transfer learning
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121324openalex-snapshot
Investigation on the abilities of different artificial intelligence methods to predict the aerodynamic coefficients
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.132624openalex-snapshot
Domain augmentation adversarial network for extrapolation modelling of aerodynamic coefficients
inferred← citeshttps://doi.org/10.1016/j.jairtraman.2023.102437openalex-snapshot
Machine learning and mixed reality for smart aviation: Applications and challenges
inferred← citeshttps://doi.org/10.1063/5.0140545openalex-snapshot
Fast aerodynamics prediction of laminar airfoils based on deep attention network
inferred← citeshttps://doi.org/10.1108/hff-08-2022-0488openalex-snapshot
Data-driven augmentation of a RANS turbulence model for transonic flow prediction
inferred← citeshttps://doi.org/10.2139/ssrn.5348348openalex-snapshot
Adaptive Model-Predictive Control Method for Aircraft Based on Intelligent Aerodynamic Identification
inferred← citeshttps://doi.org/10.2139/ssrn.5370691openalex-snapshot
Aerodynamic Performance Prediction of Airfoils with Transformers
inferred← citeshttps://doi.org/10.23967/eccomas.2022.077openalex-snapshot
Data-Driven Reduced Order Modeling for Aerodynamic Flow Predictions
inferred← citeshttps://doi.org/10.2514/1.c038456openalex-snapshot
Enhanced Computational Performance and Stability & Control Prediction for NATO Military Vehicles
inferred← citeshttps://doi.org/10.2514/6.2022-3899openalex-snapshot
Data-Driven Aerodynamic Modeling Using the DLR SMARTy Toolbox
inferred← citeshttps://doi.org/10.2514/6.2023-0820openalex-snapshot
Introduction to AVT-351: Enhanced Computational Performance and Stability & Control Prediction for NATO Military Vehicles
inferred← citeshttps://doi.org/10.2514/6.2024-0010openalex-snapshot
Recent Advances in Data-Driven Modeling for Aerodynamic Applications using DLR’s SMARTy Toolbox
inferred← citeshttps://doi.org/10.3390/act11020046openalex-snapshot
Experimental Study of Dynamical Airfoil and Aerodynamic Prediction
inferred← citeshttps://doi.org/10.7712/140123.10188.18859openalex-snapshot
PHYSICS-BASED REGULARIZATION OF NEURAL NETWORKS FOR AERODYNAMIC FLOW PREDICTIONS
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