Saved in:
| Main Authors: | Jangir, A., Clements, R., Goyal, R., Tabor, G. |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.04670 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows
by: Su, Zijie, et al.
Published: (2024)
by: Su, Zijie, et al.
Published: (2024)
Effects of Annulation on Low-Reynolds-Number Flows over an Orthocone
by: Thakor, M., et al.
Published: (2023)
by: Thakor, M., et al.
Published: (2023)
Numerical Solutions of 2-D Steady Incompressible Driven Cavity Flow at High Reynolds Numbers
by: Erturk, E., et al.
Published: (2004)
by: Erturk, E., et al.
Published: (2004)
Sensitivity of Isothermal Swirl Combustor Flow to Inlet Reynolds Number
by: Mahato, Madan Lal, et al.
Published: (2026)
by: Mahato, Madan Lal, et al.
Published: (2026)
A Simple but Efficient Transformer-Based Physics-Informed Neural Network for Incompressible Navier--Stokes Equations
by: Barman, Biswanath, et al.
Published: (2026)
by: Barman, Biswanath, et al.
Published: (2026)
Turbulence at Low Reynolds Numbers
by: Yu, Ziyue, et al.
Published: (2025)
by: Yu, Ziyue, et al.
Published: (2025)
Large Eddy Simulations of Turbulent Pipe Flows At Moderate-To-High Reynolds Numbers
by: Garg, Himani, et al.
Published: (2022)
by: Garg, Himani, et al.
Published: (2022)
Flow Topology Optimization at High Reynolds Numbers Based on Modified Turbulence Models
by: Wu, Chenyu, et al.
Published: (2022)
by: Wu, Chenyu, et al.
Published: (2022)
An Efficient Wavelet-based Physics Informed Residual Neural Networks for Flow Field Reconstruction with Extremely Sparse Data
by: Barman, Biswanath, et al.
Published: (2026)
by: Barman, Biswanath, et al.
Published: (2026)
Mach and Reynolds Number Effects on Transonic Buffet on the XRF-1 Transport Aircraft Wing at Flight Reynolds Number
by: Waldmann, Andreas, et al.
Published: (2022)
by: Waldmann, Andreas, et al.
Published: (2022)
Reynolds Number Effects on Lift Enhancement Mechanisms of Dragonfly Wings: Their Effective Ranges and Determination by Local Reynolds Numbers
by: Fujita, Yusuke, et al.
Published: (2025)
by: Fujita, Yusuke, et al.
Published: (2025)
Lagrangian Particle Tracking at Large Reynolds Numbers
by: Küchler, Christian, et al.
Published: (2024)
by: Küchler, Christian, et al.
Published: (2024)
Physics-Informed Neural Networks for Transonic Flows around an Airfoil
by: Wassing, Simon, et al.
Published: (2024)
by: Wassing, Simon, et al.
Published: (2024)
Unsteady flow predictions around an obstacle using Geometry-Parameterized Dual-Encoder Physics-Informed Neural Network
by: Wang, Zekun, et al.
Published: (2026)
by: Wang, Zekun, 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)
A Fourier/Modal-Spectral-Element Method for the Simulation of High-Reynolds Number Incompressible Stratified Flows in Domains with a Single Non-Periodic Direction
by: Reyes-Gil, Nidia, et al.
Published: (2025)
by: Reyes-Gil, Nidia, et al.
Published: (2025)
The Footprint of Laminar Separation on a Wall-Bounded Wing Section at Transitional Reynolds Numbers
by: Klewicki, Charles, et al.
Published: (2024)
by: Klewicki, Charles, et al.
Published: (2024)
Active Flow Control for Bluff Body under High Reynolds Number Turbulent Flow Conditions Using Deep Reinforcement Learning
by: Chen, Jingbo, et al.
Published: (2024)
by: Chen, Jingbo, et al.
Published: (2024)
A Helicity-Conservative Domain-Decomposed Physics-Informed Neural Network for Incompressible Non-Newtonian Flow
by: Lu, Zheng, et al.
Published: (2026)
by: Lu, Zheng, et al.
Published: (2026)
Identification of Settling Velocity with Physics Informed Neural Networks For Sediment Laden Flows
by: Delcey, Mickaël, et al.
Published: (2024)
by: Delcey, Mickaël, 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)
Project and Generate: Divergence-Free Neural Operators for Incompressible Flows
by: Li, Xigui, et al.
Published: (2026)
by: Li, Xigui, et al.
Published: (2026)
Physics Informed Neural Networks for Free Shear Flows
by: Raghu, Siddharth, et al.
Published: (2024)
by: Raghu, Siddharth, et al.
Published: (2024)
Characteristic Bending in Incompressible Flows
by: Blomquist, Matthew, et al.
Published: (2025)
by: Blomquist, Matthew, et al.
Published: (2025)
Robust and Adaptive Deep Reinforcement Learning for Enhancing Flow Control around a Square Cylinder with Varying Reynolds Numbers
by: Jia, Wang, et al.
Published: (2024)
by: Jia, Wang, et al.
Published: (2024)
Physics-Informed Neural Networks for Weakly Compressible Flows Using Galerkin-Boltzmann Formulation
by: Aygun, Atakan, et al.
Published: (2024)
by: Aygun, Atakan, et al.
Published: (2024)
Weak-Strong Uniqueness and Extreme Wall Events at High Reynolds Number
by: Eyink, Gregory L., et al.
Published: (2025)
by: Eyink, Gregory L., et al.
Published: (2025)
Heavy Particle Clustering in Inertial Subrange of High--Reynolds Number Turbulence
by: Matsuda, Keigo, et al.
Published: (2024)
by: Matsuda, Keigo, et al.
Published: (2024)
Implicit Incompressible Porous Flow using SPH
by: Böttcher, Timna, et al.
Published: (2025)
by: Böttcher, Timna, et al.
Published: (2025)
Integrating Discrete Sub-grid Filters with Discretization-Corrected Particle Strength Exchange Method for High Reynolds Number Flow Simulations
by: Obeidat, Anas
Published: (2024)
by: Obeidat, Anas
Published: (2024)
Experimental Investigation of Tidally-Forced Internal Wave Turbulence at High Reynolds Number
by: Taebel, Zachary, et al.
Published: (2024)
by: Taebel, Zachary, et al.
Published: (2024)
An Algebraic Non-Equilibrium Turbulence Model of the High Reynolds Number Transition Region
by: Basse, Nils T.
Published: (2024)
by: Basse, Nils T.
Published: (2024)
Compressibility Effects on Leading-Edge Dynamic Stall Criteria at High Reynolds Number
by: Sudharsan, Sarasija, et al.
Published: (2025)
by: Sudharsan, Sarasija, et al.
Published: (2025)
Optimizing Metachronal Paddling with Reinforcement Learning at Low Reynolds Number
by: Bailey, Alana A., et al.
Published: (2025)
by: Bailey, Alana A., et al.
Published: (2025)
Learnable Viscosity Modulation in Physics-Informed Neural Networks for Incompressible Flow Reconstruction
by: Xu, Ke, et al.
Published: (2026)
by: Xu, Ke, et al.
Published: (2026)
A Thermomechanical Hybrid Incompressible Material Point Method
by: Kala, Victoria, et al.
Published: (2024)
by: Kala, Victoria, et al.
Published: (2024)
Geometrically Parametrised Reduced Order Models for the Study of Hysteresis of the Coanda Effect in Finite Element-based Incompressible Fluid Dynamics
by: Bravo, J. R., et al.
Published: (2023)
by: Bravo, J. R., et al.
Published: (2023)
Phase-based analysis and control of low Reynolds number aeroelastic flows
by: Sumanasiri, Chathura R., et al.
Published: (2025)
by: Sumanasiri, Chathura R., et al.
Published: (2025)
Efficient Computation of Large-Scale Statistical Solutions to Incompressible Fluid Flows
by: Rohner, Tobias, et al.
Published: (2024)
by: Rohner, Tobias, et al.
Published: (2024)
Wake Stabilization and Force Modulation via Surface Dimples on an Airfoil at Low-Reynolds-Numbers
by: Sudarsana, Putu Brahmanda, et al.
Published: (2025)
by: Sudarsana, Putu Brahmanda, et al.
Published: (2025)
Similar Items
-
Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows
by: Su, Zijie, et al.
Published: (2024) -
Effects of Annulation on Low-Reynolds-Number Flows over an Orthocone
by: Thakor, M., et al.
Published: (2023) -
Numerical Solutions of 2-D Steady Incompressible Driven Cavity Flow at High Reynolds Numbers
by: Erturk, E., et al.
Published: (2004) -
Sensitivity of Isothermal Swirl Combustor Flow to Inlet Reynolds Number
by: Mahato, Madan Lal, et al.
Published: (2026) -
A Simple but Efficient Transformer-Based Physics-Informed Neural Network for Incompressible Navier--Stokes Equations
by: Barman, Biswanath, et al.
Published: (2026)