mlsanityData to 7 Oct 2026

Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations

arXiv 2407.19916 ↗First posted 29 Jul 2024cs.CE cs.LG cs.NAcited by 24

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Abstract (arXiv)

This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The proposed models can be applied directly to unstructured domains for different flow conditions, handle non-parametric 3D geometric variations, and generalize to unseen shapes at test time. The coordinate-based formulation naturally leads to robustness with respect to discretization, allowing an excellent trade-off between computational cost (memory footprint and training time) and accuracy. The method is demonstrated on two industrially relevant applications: a RANS dataset of the two-dimensional compressible flow over a transonic airfoil and a dataset of the surface pressure distribution over 3D wings, including shape, inflow condition, and control surface deflection variations. On the considered test cases, our approach achieves a more than three times lower test error and significantly improves generalization error on unseen geometries compared to state-of-the-art Graph Neural Network architectures. Remarkably, the method can perform inference five order of magnitude faster than the high fidelity solver on the RANS transonic airfoil dataset. Code is available at https://gitlab.isae-supaero.fr/gi.catalani/aero-nepf

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 33 of its 67 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

33 of 67 references.

References · 33

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
  5. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  6. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
  7. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  8. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  9. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  10. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  11. 2020Learning Mesh-Based Simulation with Graph Networkscited by 840
  12. 2019Graph U-Netscited by 489
21 more
  1. 2023Geometry-Informed Neural Operator for Large-Scale 3D PDEscited by 293
  2. 2020On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networkscited by 279
  3. 2023A Survey on Oversmoothing in Graph Neural Networkscited by 246
  4. 2020Learned Initializations for Optimizing Coordinate-Based Neural Representationscited by 240
  5. 2023Neural Operators for Accelerating Scientific Simulations and Designcited by 218
  6. 2022From data to functa: Your data point is a function and you can treat it like onecited by 183
  7. 2018Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flowscited by 147
  8. 2020A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEscited by 105
  9. 2018Towards Sparse Hierarchical Graph Classifierscited by 100
  10. 2022MultiScale MeshGraphNetscited by 94
  11. 2023Operator Learning with Neural Fields: Tackling PDEs on General Geometriescited by 80
  12. 2021SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimizationcited by 59
  13. 2024Geom-DeepONet: A Point-cloud-based Deep Operator Network for Field Predictions on 3D Parameterized Geometriescited by 47
  14. 2021Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flowscited by 44
  15. 2018Geodesic Convolutional Shape Optimizationcited by 37
  16. 2023SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processescited by 27
  17. 2019Data-Driven POD-Galerkin Reduced Order Model for Turbulent Flowscited by 21
  18. 2022PINs: Progressive Implicit Networks for Multi-Scale Neural Representationscited by 19
  19. 2023MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilitycited by 15
  20. 2022A composable machine-learning approach for steady-state simulations on high-resolution gridscited by 8
  21. 2023INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equationscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 13; 2026: 11.

24 papers.

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  1. 2025BlendedNet: A Blended Wing Body Aircraft Dataset and Surrogate Model for Aerodynamic Predictionscited by 16
  2. 2025TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networkscited by 14
  3. 2025Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulationscited by 10
  4. 2026Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operatorscited by 6
  5. 2025A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary Geometriescited by 5
  6. 2025Geometry aware inference of steady state PDEs using Equivariant Neural Fields representationscited by 5
  7. 2025SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic designcited by 2
  8. 2026AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modelingcited by 2
  9. 2025FA-INR: Adaptive Implicit Neural Representations for Interpretable Exploration of Simulation Ensemblescited by 1
  10. 2026A Geometry-Aware Triplane Field Network for Vehicle Aerodynamic Predictioncited by 1
  11. 2025PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulationscited by 0
  12. 2025Physics-based machine learning for mantle convection simulationscited by 0
12 more
  1. 2025NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysiscited by 0
  2. 2025Computational Fluid Dynamics Optimization of F1 Front Wing using Physics Informed Neural Networkscited by 0
  3. 2025BlendedNet++: A dataset and benchmark for field-resolved aerodynamics and inverse design of blended wing body aircraftcited by 0
  4. 2025A Multi-fidelity Double-Delta Wing Dataset and Empirical Scaling Laws for GNN-based Aerodynamic Field Surrogatecited by 0
  5. 2026Signal-Aware Conditional Diffusion Surrogates for Transonic Wing Pressure Predictioncited by 0
  6. 2026APIC: Amortized Physics-Informed Calibration using Neural Processescited by 0
  7. 2026Geometry-Aware Anisotropic Boundary Correction for Aerodynamic Simulationcited by 0
  8. 2026GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problemscited by 0
  9. 2026CoFINN: Conservation Flux Informed Neural Networks for Physics Problems Governed by Conservation Lawscited by 0
  10. 2026Spatio-Temporal Prediction of Unsteady Airfoil Aerodynamics Using Augmented Graph Neural Ordinary Differential Equations with Exogenous Controlscited by 0
  11. 2026Neural Field Ensembles for Aerodynamic Surface Prediction: Winning Solution to the ONERA CRM Wall Distribution 2025 Challengecited by 0
  12. 2026Airfoil2Vec: Spectral Geometry-Conditioned Neural Surrogate Models for Airfoil Aerodynamics and a Downforce-Generating CFD Datasetcited by 0