Copertina

Solving compressible Navier-Stokes equations using the feature-enhanced neural network

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Song, Jiahao, Cao, Wenbo, Zhang, Weiwei
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Fluid Dynamics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

FENN: Feature-enhanced neural network for solving partial differential equations involving fluid mechanics
di: Song, Jiahao, et al.
Pubblicazione: (2025)

A solver for subsonic flow around airfoils based on physics-informed neural networks and mesh transformation
di: Cao, Wenbo, et al.
Pubblicazione: (2024)

A complete state-space solution model for inviscid flow around airfoils based on physics-informed neural networks
di: Cao, Wenbo, et al.
Pubblicazione: (2024)

A matrix preconditioning framework for physics-informed neural networks based on adjoint method
di: Song, Jiahao, et al.
Pubblicazione: (2025)

An analysis and solution of ill-conditioning in physics-informed neural networks
di: Cao, Wenbo, et al.
Pubblicazione: (2024)

Solving all laminar flows around airfoils all-at-once using a parametric neural network solver
di: Cao, Wenbo, et al.
Pubblicazione: (2025)

Accelerating high order discontinuous Galerkin solvers using neural networks: 3D compressible Navier-Stokes equations
di: de Lara, Fernando Manrique, et al.
Pubblicazione: (2022)

Self-adaptive loss balanced Physics-informed neural networks for the incompressible Navier-Stokes equations
di: Xiang, Zixue, et al.
Pubblicazione: (2021)

Stochastic compressible Navier-Stokes equations under location uncertainty and their approximations for ocean modelling
di: Tissot, Gilles, et al.
Pubblicazione: (2023)

Whistling of turbulent cavity flows: Self-consistent predictions with the linearized compressible Navier-Stokes equations
di: Bozikis, Nikolaos, et al.
Pubblicazione: (2025)

Finite difference physics-informed neural networks enable improved solution accuracy of the Navier-Stokes equations
di: Roy, Nityananda, et al.
Pubblicazione: (2024)

Quantum spin representation for the Navier-Stokes equation
di: Meng, Zhaoyuan, et al.
Pubblicazione: (2024)

Structure tensor Reynolds-averaged Navier-Stokes turbulence models with equivariant neural networks
di: Miller, Aaron, et al.
Pubblicazione: (2025)

Analysis of accuracy and efficiency of neural networks to simulate Navier-Stokes fluid flows with obstacles
di: Hespanha, Rui, et al.
Pubblicazione: (2025)

New Interpretation for error propagation of data-driven Reynolds stress closures via global stability analysis
di: Shan, Xianglin, et al.
Pubblicazione: (2024)

Positivity-preserving and entropy-bounded discontinuous Galerkin method for the chemically reacting, compressible Navier-Stokes equations
di: Ching, Eric J., et al.
Pubblicazione: (2023)

Spontaneous stochasticity in the fluctuating Navier-Stokes equations on a logarithmic lattice
di: Ortiz, Erika, et al.
Pubblicazione: (2025)

Is it true that no mathematical relation exists between the Navier-Stokes equations and the multifractal model?
di: Gibbon, John D., et al.
Pubblicazione: (2026)

Deep learning accelerated solutions of incompressible Navier-Stokes equations on non-uniform Cartesian grids
di: Bai, Heming, et al.
Pubblicazione: (2026)

WAN3DNS: Weak Adversarial Networks for Solving 3D Incompressible Navier-Stokes Equations
di: Li, Wenran, et al.
Pubblicazione: (2025)

Conservative and skew-symmetric forms of the incompressible Navier-Stokes equations in sigma-coordinates
di: Jung, Jaeyoung, et al.
Pubblicazione: (2026)

Two-relaxation-time regularized lattice Boltzmann model for Navier-Stokes equations
di: Yu, Yuan, et al.
Pubblicazione: (2023)

High-efficient machine learning projection method for incompressible Navier-Stokes equations
di: Chen, Ruilin
Pubblicazione: (2025)

Space-local Navier--Stokes turbulence
di: Araki, Ryo, et al.
Pubblicazione: (2023)

Estimates for the 2D Navier-Stokes equations: the effects of forcing
di: Mukherjee, Ritwik, et al.
Pubblicazione: (2025)

Diffusion-free boundary conditions for the Navier-Stokes equations
di: Dormy, Emmanuel, et al.
Pubblicazione: (2025)

Multi-stream physics hybrid networks for solving Navier-Stokes equations
di: Sedykh, Aleksandr, et al.
Pubblicazione: (2025)

Ultra-chaotic property of Navier-Stokes turbulence
di: Qin, Shijie, et al.
Pubblicazione: (2025)

A simple and efficient second-order immersed-boundary method for the incompressible Navier--Stokes equations
di: Luchini, Paolo, et al.
Pubblicazione: (2025)

A Geometric Approach to the Navier-Stokes Equations
di: Sacasa-Cespedes, Sebastian Ali
Pubblicazione: (2024)

Spectrum correction in Ekman-Navier-Stokes turbulence
di: Valadão, V. J., et al.
Pubblicazione: (2024)

A data free neural operator enabling fast inference of 2D and 3D Navier Stokes equations
di: Choi, Junho, et al.
Pubblicazione: (2025)

Schrödinger-Navier-Stokes Equation for the Quantum Simulation of Navier-Stokes Flows
di: Cappelli, Luca, et al.
Pubblicazione: (2026)

The production of uncertainty in three-dimensional Navier-Stokes turbulence
di: Ge, Jin, et al.
Pubblicazione: (2023)

Multi-fidelity graph-based neural networks architectures to learn Navier-Stokes solutions on non-parametrized 2D domains
di: Songia, Francesco, et al.
Pubblicazione: (2026)

A canonical Hamiltonian formulation of the Navier-Stokes problem
di: Sanders, John W., et al.
Pubblicazione: (2023)

Unconditionally stable symplectic integrators for the Navier-Stokes equations and other dissipative systems
di: Sungkeetanon, Sutthikiat, et al.
Pubblicazione: (2024)

Prediction of three-dimensional chemically reacting compressible turbulence based on implicit U-Net enhanced Fourier neural operator
di: Zhang, Zhiyao, et al.
Pubblicazione: (2024)

Optimization-Based Discovery of A Non-Attracting Flow State in An Oscillating-Cylinder Wake
di: Dong, Daiwei, et al.
Pubblicazione: (2026)

Forward self-similar solutions to the 2D Navier--Stokes equations
di: Albritton, Dallas, et al.
Pubblicazione: (2026)