The paper introduces a correction framework that grounds a CFD-trained deep learning surrogate model for aerospace aerodynamics using wind‑tunnel pressure‑sensor (PSP) data. By training a correction network on spatially registered PSP measurements at two Mach numbers, the authors adjust the surrogate’s predictions without retraining its core parameters, achieving improved agreement with experimental pressure distributions—especially at the wing suction peak and shock location. The grounded surrogate matches measurements within 2.3–2.7% of the Cp range on unseen angles of attack and outperforms simple interpolation between measured states.
By Nitin Nagesh Kulkarni, Dheeraj Vemula, Yin Yu, Peter Lyu, Juan J. Alonso
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...
By Lionel Salesses, Caroline Sainvitu, Tariq Benamara
arXiv:2609.08947v1 Announce Type: cross
Abstract: Graph-based surrogate models offer a promising route to accelerate computational fluid dynamics (CFD) simulations on unstructured meshes. However, th...
By Th\'eodore Michel, Antoine Campos, Alban Dujardin, Henry Areiza, Philippe Meliga, Elie Hachem
The paper explores using residual learning with an LSTM neural network to enhance unsteady aerodynamic load predictions for aeroelastic applications. By training the network on the difference between high‑fidelity CFD lift data and a physics‑based Wagner model for the NLR 7301 airfoil in transonic flow, the residual approach consistently outperforms a direct neural‑network model in most tests, especially in generalization scenarios. The study demonstrates that residual learning can effectively augment classical low‑order aerodynamic theories by learning a lower‑variance correction to the baseline physics model.
By Divya Sanghi, Carlos E. S. Cesnik
ChannelFlow-Tools is an open‑source, configuration‑driven pipeline that generates machine‑learning‑ready datasets for three‑dimensional obstructed channel flows. It combines procedural obstacle geometry generation across six shape families, signed‑distance‑field voxelisation, lattice‑Boltzmann simulation, and packaging into ML‑ready tensors, all driven by reproducible configuration files. The pipeline is validated through mesh‑integrity audits, SDF representation checks, solver benchmarks, and data‑integrity audits, and it has been used to train surrogate models (3D U‑Net, FNO, U‑FNO) that learn geometry‑to‑flow mappings and exhibit physically interpretable behaviour on out‑of‑distribution splits.
By Shubham Kavane, Lukas Schr\"oder, Kajol Kulkarni, Fernando Gonzalez, Harald Koestler
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.
By Mohamed Elrefaie, Dule Shu, Matt Klenk, Faez Ahmed
arXiv:2505.14717v2 Announce Type: replace-cross
Abstract: Scientific machine learning uses simulation data to train surrogate models for fast physical-field prediction across geometries. Local shape...
By Xigui Li, Yuanye Zhou, Feiyang Xiao, Xin Guo, Chen Jiang, Tan Pan, Xingmeng Zhang, Cenyu Liu, Zeyun Miao, Xiansheng Wang, Qimeng Wang, Yichi Zhang, Wenbo Zhang, Hongwei Zhang, Ruoxi Jiang, Fengping Zhu, Limei Han, Chensen Lin, Yuan Cheng
The paper explores using residual learning with an LSTM neural network to enhance unsteady aerodynamic load predictions for aeroelastic applications. By training the network on the difference between high‑fidelity CFD lift data and a physics‑based Wagner model, the residual approach outperforms a direct neural‑network model in most cases, especially in generalization tests. The study demonstrates that aligning residual inputs with the baseline physics variables yields lower error and more consistent performance across varied motion scenarios.
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.
By Javier Nieto-Centenero, Esther Andr\'es, Rodrigo Castellanos
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...
By Wenxuan Jin, Jianguo Yao, Haibing Guan, Xijun Li
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.
By Kangkang Qi, Huiyu Yang, Keqi Ding, Yunpeng Wang, Yuntian Chen, Yuanwei Bin, Rikui Zhang, Jianchun Wang
The paper introduces VATO, a Vortex-Force-Aware Transformer Operator designed to predict unsteady separated flows around aerofoils more accurately. VATO couples a Vortex Force Map (VFM) method with a geometry-aware neural operator, offering two variants: VATO‑S, which adds training-only supervision of local VFM force contributions, and VATO‑A, which prioritizes force-relevant source locations for residual cross attention. Evaluated on CFD data for double‑edged‑plate aerofoils, VATO‑S and VATO‑A reduce velocity, pressure, and vorticity errors by up to 15.8%, 7.5%, and 31.2% respectively, and improve aerodynamic force predictions even beyond the training range.
By Xingxin Yang, Zhan Zhang, Yichen Li, Juan Li