Saved in:
| Main Authors: | Sharpe, Peter, Hansman, R. John |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.16323 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FoilDiff: A Hybrid Transformer Backbone for Diffusion-based Modelling of 2D Airfoil Flow Fields
by: Ogbuagu, Kenechukwu, et al.
Published: (2025)
by: Ogbuagu, Kenechukwu, et al.
Published: (2025)
GLOBE: Accurate and Generalizable PDE Surrogates using Domain-Inspired Architectures and Equivariances
by: Sharpe, Peter
Published: (2025)
by: Sharpe, Peter
Published: (2025)
Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning
by: Liu, Xuemin, et al.
Published: (2025)
by: Liu, Xuemin, et al.
Published: (2025)
Accelerating Transient CFD through Machine Learning-Based Flow Initialization
by: Sharpe, Peter, et al.
Published: (2025)
by: Sharpe, Peter, et al.
Published: (2025)
HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
by: Ashton, Neil, et al.
Published: (2026)
by: Ashton, Neil, et al.
Published: (2026)
Airfoil optimization using Design-by-Morphing with minimized design-space dimensionality
by: Lee, Sangjoon, et al.
Published: (2025)
by: Lee, Sangjoon, et al.
Published: (2025)
Accelerated Airfoil Design Using Neural Network Approaches
by: Patel, Anantram, et al.
Published: (2025)
by: Patel, Anantram, et al.
Published: (2025)
Hard Constraint Projection in a Physics Informed Neural Network
by: Horne, Miranda J. S., et al.
Published: (2026)
by: Horne, Miranda J. S., et al.
Published: (2026)
A Kernel-based Resource-efficient Neural Surrogate for Multi-fidelity Prediction of Aerodynamic Field
by: Sarker, Apurba, et al.
Published: (2025)
by: Sarker, Apurba, et al.
Published: (2025)
Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learning
by: Mondal, Trishit, et al.
Published: (2025)
by: Mondal, Trishit, et al.
Published: (2025)
Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows
by: Liu, Zhecheng, et al.
Published: (2024)
by: Liu, Zhecheng, et al.
Published: (2024)
Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurements
by: Mousavi, Hanieh, et al.
Published: (2025)
by: Mousavi, Hanieh, et al.
Published: (2025)
C(NN)FD -- Deep Learning Modelling of Multi-Stage Axial Compressors Aerodynamics
by: Bruni, Giuseppe, et al.
Published: (2025)
by: Bruni, Giuseppe, et al.
Published: (2025)
Physics-Informed Neural Networks for Transonic Flows around an Airfoil
by: Wassing, Simon, et al.
Published: (2024)
by: Wassing, Simon, et al.
Published: (2024)
LGFNet: Local-Global Fusion Network with Fidelity Gap Delta Learning for Multi-Source Aerodynamics
by: Zhu, Qinye, et al.
Published: (2026)
by: Zhu, Qinye, et al.
Published: (2026)
TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks
by: Chen, Qian, et al.
Published: (2025)
by: Chen, Qian, et al.
Published: (2025)
Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates
by: Fan, Lushun, et al.
Published: (2025)
by: Fan, Lushun, et al.
Published: (2025)
Investigation of Compressor Cascade Flow Using Physics- Informed Neural Networks with Adaptive Learning Strategy
by: Li, Zhihui, et al.
Published: (2023)
by: Li, Zhihui, et al.
Published: (2023)
Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design
by: Yang, Yunjia, et al.
Published: (2026)
by: Yang, Yunjia, et al.
Published: (2026)
Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations
by: Chen, Biao, et al.
Published: (2026)
by: Chen, Biao, et al.
Published: (2026)
Machine Learning Visualization Tool for Exploring Parameterized Hydrodynamics
by: Jekel, C. F., et al.
Published: (2024)
by: Jekel, C. F., et al.
Published: (2024)
Aerodynamic Control of Laminar Separation on a Wall-Bounded Airfoil at Transitional Reynolds Numbers
by: Klewicki, Charles, et al.
Published: (2025)
by: Klewicki, Charles, et al.
Published: (2025)
NeurIPS 2024 ML4CFD Competition: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Design
by: Yagoubi, Mouadh, et al.
Published: (2024)
by: Yagoubi, Mouadh, et al.
Published: (2024)
Spectrally Informed Learning of Fluid Flows
by: Shaffer, Benjamin D., et al.
Published: (2024)
by: Shaffer, Benjamin D., et al.
Published: (2024)
Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics
by: Chou, Yi En, et al.
Published: (2025)
by: Chou, Yi En, et al.
Published: (2025)
Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks
by: Wang, Chuanxing, et al.
Published: (2025)
by: Wang, Chuanxing, et al.
Published: (2025)
Physics-Informed Graph Neural Network Surrogates for Turbulent Nanoparticle Dispersion in Dental Clinical Environments
by: Shende, Takshak, et al.
Published: (2026)
by: Shende, Takshak, et al.
Published: (2026)
A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems
by: Menicali, Luca, et al.
Published: (2025)
by: Menicali, Luca, et al.
Published: (2025)
Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study
by: Abbasi, Jassem, et al.
Published: (2025)
by: Abbasi, Jassem, et al.
Published: (2025)
Bayesian Reasoning for Physics Informed Neural Networks
by: Graczyk, Krzysztof M., et al.
Published: (2023)
by: Graczyk, Krzysztof M., et al.
Published: (2023)
Multi-scale Dynamic Wake Modeling and Prediction of Floating Offshore Wind Turbines via Physics-Informed Neural Networks and Fourier Neural Operators
by: Dong, Guodan, et al.
Published: (2026)
by: Dong, Guodan, et al.
Published: (2026)
Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design
by: Mondal, Sudeepta, et al.
Published: (2026)
by: Mondal, Sudeepta, et al.
Published: (2026)
Physics-Informed Neural Network Approaches for Sparse Data Flow Reconstruction of Unsteady Flow Around Complex Geometries
by: Malineni, Vamsi Sai Krishna, et al.
Published: (2025)
by: Malineni, Vamsi Sai Krishna, et al.
Published: (2025)
Physics-Guided Machine Learning for Uncertainty Quantification in Turbulence Models
by: Chu, Minghan, et al.
Published: (2025)
by: Chu, Minghan, et al.
Published: (2025)
Flow Field Reconstruction via Voronoi-Enhanced Physics-Informed Neural Networks with End-to-End Sensor Placement Optimization
by: Xiao, Renjie, et al.
Published: (2026)
by: Xiao, Renjie, et al.
Published: (2026)
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
by: Ohana, Ruben, et al.
Published: (2024)
by: Ohana, Ruben, et al.
Published: (2024)
Investigation of Numerical Diffusion in Aerodynamic Flow Simulations with Physics Informed Neural Networks
by: Warey, Alok, et al.
Published: (2021)
by: Warey, Alok, et al.
Published: (2021)
Solving the Discretised Multiphase Flow Equations with Interface Capturing on Structured Grids Using Machine Learning Libraries
by: Chen, Boyang, et al.
Published: (2024)
by: Chen, Boyang, et al.
Published: (2024)
Data-Driven Discovery and Formulation Refines the Quasi-Steady Model of Flapping-Wing Aerodynamics
by: Kamimizu, Yu, et al.
Published: (2025)
by: Kamimizu, Yu, et al.
Published: (2025)
SIMR-NO: A Spectrally-Informed Multi-Resolution Neural Operator for Turbulent Flow Super-Resolution
by: Abid, Muhammad, et al.
Published: (2026)
by: Abid, Muhammad, et al.
Published: (2026)
Similar Items
-
FoilDiff: A Hybrid Transformer Backbone for Diffusion-based Modelling of 2D Airfoil Flow Fields
by: Ogbuagu, Kenechukwu, et al.
Published: (2025) -
GLOBE: Accurate and Generalizable PDE Surrogates using Domain-Inspired Architectures and Equivariances
by: Sharpe, Peter
Published: (2025) -
Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning
by: Liu, Xuemin, et al.
Published: (2025) -
Accelerating Transient CFD through Machine Learning-Based Flow Initialization
by: Sharpe, Peter, et al.
Published: (2025) -
HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
by: Ashton, Neil, et al.
Published: (2026)