arXiv AI
Jul 14
IG-GAN: A Generative Adversarial Network for Aerodynamic Data Generation Based on Intrinsic Geometry
arXiv:2607. 11497v1 Announce Type: cross Abstract: Existing generative models learn data distributions in flat Euclidean space.
Condition-Enhanced Generative Adversarial Network for Aerodynamic Configuration Design of Commercial Aircraft
Related stories
arXiv AI
Jul 14
Knowledge-Constrained Shape Optimization with a Mixture-of-Experts Neural Operator for High-Confidence Design
arXiv:2607. 09763v1 Announce Type: cross Abstract: Engineering shape optimization faces challenges in both expert-dependent problem setup and surrogate-model reliability.
arXiv Machine Learning
Sep 16
Neural Field Ensembles for Aerodynamic Surface Prediction: Winning Solution to the ONERA CRM Wall Distribution 2025 Challenge
arXiv:2609.17160v1 Announce Type: new Abstract: Machine-learning surrogate models offer a promising alternative to high-fidelity Computational Fluid Dynamics (CFD) simulations for aerodynamic analysi...
arXiv Machine Learning
Oct 2
Airfoil2Vec: Spectral Geometry-Conditioned Neural Surrogate Models for Airfoil Aerodynamics and a Downforce-Generating CFD Dataset
The paper presents a dataset of roughly 10,000 RANS simulations of steady, incompressible, two‑dimensional subsonic flow around downforce‑generating NACA 4‑digit airfoils, aimed at automotive and motorsport regimes. It explores geometry‑conditioned neural surrogates—comparing neural fields, neural ODEs, MLPs, graph models, and spectral conditioning—and introduces Airfoil2Vec, a spectral geometry encoder that fuses joint contour, camber, and thickness spectra to predict continuous pressure and velocity fields. The surrogate models are evaluated on angle‑of‑attack interpolation, unseen NACA geometries, and non‑NACA airfoils, achieving accurate aerodynamic predictions with orders‑of‑magnitude speedups over traditional CFD.
arXiv Machine Learning
Jun 15
Multi-fidelity aerodynamic data fusion by autoencoder transfer learning
arXiv:2512. 13069v2 Announce Type: replace Abstract: Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling.
arXiv AI
Sep 10
FSAN: Flow State Attention Network for Aerodynamic Prediction
arXiv:2609.06660v1 Announce Type: cross Abstract: Accurate aerodynamic prediction is critical for designing fuel-efficient and safe transportation systems such as aircraft and automobiles, yet tradit...
arXiv Machine Learning
Jun 9
A Geometry-Aware Triplane Field Network for Vehicle Aerodynamic Prediction
arXiv:2606. 07724v1 Announce Type: new Abstract: High-fidelity computational fluid dynamics (CFD) is crucial to vehicle aerodynamic analysis, but its cost still constrains early-stage design exploration.
arXiv AI
Oct 15
Neural-Surrogate Co-Design of Aerodynamic Shape and Control
arXiv:2603.06760v2 Announce Type: replace-cross Abstract: Control co-design jointly optimizes a robot's physical design and its controller. Existing techniques are limited in the number of model para...
arXiv Machine Learning
Aug 21
CarBench: A Comprehensive Benchmark for Neural Surrogates on High-Fidelity 3D Car Aerodynamics
arXiv:2512. 07847v2 Announce Type: replace Abstract: Benchmarking has been the cornerstone of progress in computer vision, natural language processing, and the broader deep learning domain, driving algorithmic innovation through standardized datasets and reproducible evaluation protocols.
arXiv Machine Learning
Jul 9
Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization
arXiv:2603. 17057v2 Announce Type: replace-cross Abstract: Active multi-fidelity surrogate modeling is developed for multi-condition airfoil shape optimization to reduce high-fidelity CFD cost while retaining RANS-consistent aerodynamic metrics.
arXiv AI
Oct 2
CANTO: CAD-Native Transformer Operators for AI-Aided Engineering
CANTO is a transformer neural operator that directly maps continuous CAD geometry—represented as sequences of NURBS patches—to physical fields without requiring meshing. It tokenizes control points, knot vectors, and weights to predict continuous surface and volume fields at arbitrary query locations. Evaluated on automotive and aircraft aerodynamics benchmarks, CANTO achieves state‑of‑the‑art accuracy, notably a 19.8% reduction in surface‑pressure error on HiLiftAeroML, and enables gradient‑based inverse design, finding lower‑drag designs on AhmedML.
arXiv Machine Learning
Jun 3
LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation
arXiv:2510. 22491v3 Announce Type: replace Abstract: Generating high-fidelity 3D geometries under explicit parameter constraints is central to engineering design, yet current methods often require large datasets and fail to provide reliable control beyond the training distribution.