Image de couverture de livre

Benchmarking machine learning models for predicting aerofoil performance

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Summerell, Oliver, Aragon-Camarasa, Gerardo, Sanchez, Stephanie Ordonez
Format:	Preprint
Publié:	2025
Sujets:	Fluid Dynamics Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Deep learning-based predictive modelling of transonic flow over an aerofoil
par: Chen, Li-Wei, et autres
Publié: (2024)

Hierarchy of extreme-event predictability in turbulence revealed by machine learning
par: Yang, Yuxuan, et autres
Publié: (2026)

Method of data forward generation with partial differential equations for machine learning modeling in fluid mechanics
par: Chen, Ruilin
Publié: (2025)

oRANS: Online optimisation of RANS machine learning models with embedded DNS data generation
par: Dehtyriov, Daniel, et autres
Publié: (2025)

Prospects of federated machine learning in fluid dynamics
par: San, Omer, et autres
Publié: (2022)

Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models
par: Sengupta, Arindam, et autres
Publié: (2025)

Application of machine learning algorithm in temperature field reconstruction
par: He, Qianyu, et autres
Publié: (2025)

Assessment of machine learning methods for state-to-state approaches
par: Campoli, Lorenzo, et autres
Publié: (2021)

Improving CFD simulations by local machine-learned correction
par: Mitra, Peetak, et autres
Publié: (2023)

A Physics-informed machine learning model for time-dependent wave runup prediction
par: Naeini, Saeed Saviz, et autres
Publié: (2024)

Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning
par: Yang, Yunjia, et autres
Publié: (2024)

An application of machine learning to the motion response prediction of floating assets
par: Morris-Thomas, Michael T. M. B., et autres
Publié: (2025)

Machine-learning-based multipoint optimization of fluidic injection parameters for improving nozzle performance
par: Yang, Yunjia, et autres
Publié: (2024)

Improving aircraft performance using machine learning: a review
par: Clainche, Soledad Le, et autres
Publié: (2022)

Generative prediction of flow fields around an obstacle using the diffusion model
par: Hu, Jiajun, et autres
Publié: (2024)

Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models
par: Abadía-Heredia, Rodrigo, et autres
Publié: (2024)

Single-snapshot machine learning for super-resolution of turbulence
par: Fukami, Kai, et autres
Publié: (2024)

Interpreting and generalizing deep learning in physics-based problems with functional linear models
par: Arzani, Amirhossein, et autres
Publié: (2023)

Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements
par: Kanin, E. A., et autres
Publié: (2022)

A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations
par: Nidhan, Sheel, et autres
Publié: (2024)

From large-eddy simulations to deep learning: A U-net model for fast urban canopy flow predictions
par: Vargiemezis, Themistoklis, et autres
Publié: (2025)

Transfer learning strategies for accelerating reinforcement-learning-based flow control
par: Salehi, Saeed
Publié: (2025)

Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flows
par: Fukami, Kai, et autres
Publié: (2025)

Online model learning with data-assimilated reservoir computers
par: Nóvoa, Andrea, et autres
Publié: (2025)

Perpendicular rod wake/aerofoil interaction: microphone array and TR-PIV insights via SPOD and beamforming analysis
par: Amaral, Filipe Ramos do, et autres
Publié: (2025)

FlowBERT: Prompt-tuned BERT for variable flow field prediction
par: Zou, Weihao, et autres
Publié: (2025)

Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
par: McGreivy, Nick, et autres
Publié: (2024)

Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality
par: Jeon, Joongoo, et autres
Publié: (2024)

Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation
par: Kohl, Georg, et autres
Publié: (2023)

Efficient temporal prediction of compressible flows in irregular domains using Fourier neural operators
par: Nie, Yifan, et autres
Publié: (2026)

Geometry-free prediction of inertial lift forces in microfluidic devices using deep learning
par: Ward-Bond, Jesse, et autres
Publié: (2026)

A novel Trunk Branch-net PINN for flow and heat transfer prediction in porous medium
par: Xing, Haoyun, et autres
Publié: (2025)

On the locality of local neural operator in learning fluid dynamics
par: Ye, Ximeng, et autres
Publié: (2023)

Long-horizon prediction of three-dimensional wall-bounded turbulence with CTA-Swin-UNet and resolvent analysis
par: Chen, Bo, et autres
Publié: (2026)

FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
par: Wang, Haixin, et autres
Publié: (2025)

Multi-fidelity aerodynamic data fusion by autoencoder transfer learning
par: Nieto-Centenero, Javier, et autres
Publié: (2025)

Physics-informed deep-learning applications to experimental fluid mechanics
par: Eivazi, Hamidreza, et autres
Publié: (2022)

FLUME-FNO: data-efficient and scalable prediction of 3D wind and temperature fields in unseen urban morphologies
par: Qin, Shaoxiang, et autres
Publié: (2025)

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries
par: Rabeh, Ali, et autres
Publié: (2024)

Inpainting physics: self-supervised learning for context-driven fluid simulation
par: Weidner, Jonas, et autres
Publié: (2026)