:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Han, Yuling, Li, Zhihui, Yu, Zhibin
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Fluid Dynamics
Accesso online:	https://arxiv.org/abs/2603.07740
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

On The Implicit Large Eddy Simulation of Turbomachinery Flows Using The Flux Reconstruction Method
di: Wang, Feng
Pubblicazione: (2024)

Investigation of Compressor Cascade Flow Using Physics- Informed Neural Networks with Adaptive Learning Strategy
di: Li, Zhihui, et al.
Pubblicazione: (2023)

Physics-Informed Deep Operator Learning for Computational Hydraulics Modeling
di: Liu, Xiaofeng, et al.
Pubblicazione: (2026)

Geometry-aware PINNs for Turbulent Flow Prediction
di: Ghosh, Shinjan, et al.
Pubblicazione: (2024)

Robust and Adaptive Deep Reinforcement Learning for Enhancing Flow Control around a Square Cylinder with Varying Reynolds Numbers
di: Jia, Wang, et al.
Pubblicazione: (2024)

Active Flow Control for Bluff Body under High Reynolds Number Turbulent Flow Conditions Using Deep Reinforcement Learning
di: Chen, Jingbo, et al.
Pubblicazione: (2024)

Towards Streaming Prediction of Oscillatory Flows: A Data Assimilation and Machine Learning Approach
di: Valero, Miguel M., et al.
Pubblicazione: (2025)

PT-PINNs: A Parametric Engineering Turbulence Solver based on Physics-Informed Neural Networks
di: Jiang, Liang, et al.
Pubblicazione: (2025)

Investigation of Numerical Diffusion in Aerodynamic Flow Simulations with Physics Informed Neural Networks
di: Warey, Alok, et al.
Pubblicazione: (2021)

A Physics-Informed Machine Learning Framework for Solid Boundary Treatment in Meshfree Particle Methods
di: Mehranfar, Nariman, et al.
Pubblicazione: (2025)

Turbulence Closure in RANS and Flow Inference around a Cylinder using PINNs and Sparse Experimental Data
di: Zhang, Z., et al.
Pubblicazione: (2025)

Efficient Estimation of the Convective Cooling Rate of Photovoltaic Arrays with Various Geometric Configurations: a Physics-Informed Machine Learning Approach
di: Wang, Dapeng, et al.
Pubblicazione: (2024)

Concerning the Use of Turbulent Flow Data for Machine Learning
di: Sardar, Mohammed, et al.
Pubblicazione: (2024)

Spectrally Informed Learning of Fluid Flows
di: Shaffer, Benjamin D., et al.
Pubblicazione: (2024)

AI-Accelerated Operator Learning Framework for Rarefied Microflows
di: Roohi, Ehsan
Pubblicazione: (2025)

Realizability-Constrained Machine Learning for Turbulence Closures in Wake Flows
di: Ansari, Talib, et al.
Pubblicazione: (2026)

Field Inversion Machine Learning for Time-Resolved Unsteady Flows in Airfoil Dynamic Stall
di: Li, Zilong, et al.
Pubblicazione: (2025)

High-Fidelity Reconstruction of Charge Boundary Layers and Sharp Interfaces in Electro-Thermal-Convective Flows via Residual-Attention PINNs
di: Zhou, Baitong, et al.
Pubblicazione: (2026)

A Divide-and-Conquer Machine Learning Approach for Modelling Turbulent Flows
di: Man, Anthony, et al.
Pubblicazione: (2024)

A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems
di: Menicali, Luca, et al.
Pubblicazione: (2025)

Physics-Informed Neural Networks for Transonic Flows around an Airfoil
di: Wassing, Simon, et al.
Pubblicazione: (2024)

Machine Learning Framework for Thrombosis Risk Prediction in Rotary Blood Pumps
di: Blum, Christopher, et al.
Pubblicazione: (2025)

Component-Based Machine Learning for Indoor Flow and Temperature Fields Prediction Latent Feature Aggregation and Flow Interaction
di: Wang, Shaofan, et al.
Pubblicazione: (2025)

NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
di: Sharpe, Peter, et al.
Pubblicazione: (2025)

Manifold Learning with Implicit Physics Embedding for Reduced-Order Flow-Field Modeling
di: Wang, Weiji, et al.
Pubblicazione: (2026)

Extraction of Moment Closures for Strongly Non-Equilibrium Flows via Machine Learning
di: Song, Hang, et al.
Pubblicazione: (2025)

Enhancing Arterial Blood Flow Simulations through Physics-Informed Neural Networks
di: Bhargava, Shivam, et al.
Pubblicazione: (2024)

Wall Modeling of Turbulent Flows with Varying Pressure Gradients Using Multi-Agent Reinforcement Learning
di: Zhou, Di, et al.
Pubblicazione: (2023)

Realizability-Informed Machine Learning for Turbulence Anisotropy Mappings
di: McConkey, Ryley, et al.
Pubblicazione: (2024)

Extending a Physics-Informed Machine Learning Network for Superresolution Studies of Rayleigh-Bénard Convection
di: Salim, Diane M., et al.
Pubblicazione: (2023)

Learning 3D Hypersonic Flow with Physics-Enhanced Neural Fields: A Case Study on the Orion Reentry Capsule
di: Borde, Haitz Sáez de Ocáriz, et al.
Pubblicazione: (2026)

A Corrected Open Boundary Framework for Lattice Boltzmann Immiscible Pseudopotential Models
di: Chen, Yizhong, et al.
Pubblicazione: (2025)

Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows
di: Su, Zijie, et al.
Pubblicazione: (2024)

Identification of Settling Velocity with Physics Informed Neural Networks For Sediment Laden Flows
di: Delcey, Mickaël, et al.
Pubblicazione: (2024)

Physics-Informed Neural Networks for Weakly Compressible Flows Using Galerkin-Boltzmann Formulation
di: Aygun, Atakan, et al.
Pubblicazione: (2024)

Improvement of Solid-Fluid Interaction Scheme in Lattice Boltzmann Immiscible Pseudopotential Models
di: Chen, Yizhong, et al.
Pubblicazione: (2025)

Physics Based & Machine Learning Methods For Uncertainty Estimation In Turbulence Modeling
di: Chu, Minghan
Pubblicazione: (2024)

Self-Supervised Learning for Effective Denoising of Flow Fields
di: Yu, Linqi, et al.
Pubblicazione: (2024)

A Meshless Solver for Blood Flow Simulations in Elastic Vessels Using Physics-Informed Neural Network
di: Zhang, Han, et al.
Pubblicazione: (2023)

Enhanced State Estimation for turbulent flows combining Ensemble Data Assimilation and Machine Learning
di: Valero, Miguel M., et al.
Pubblicazione: (2025)