Imagen de Portada

Meta-PINNs: Meta-Learning Enhanced Physics-Informed Machine Learning Framework for Turbomachinery Flow Predictions under Varying Operation Conditions

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Han, Yuling, Li, Zhihui, Yu, Zhibin
Formato:	Preprint
Publicado:	2026
Materias:	Fluid Dynamics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Uncertainty Quantification in Computational Fluid Dynamics: Physics and Machine Learning Based Approaches
por: Chu, Minghan
Publicado: (2023)

A Workflow for Utilizing OpenFOAM Data Structure in Physics-Informed Deep Learning Training
por: Mao, Yijin, et al.
Publicado: (2024)

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

Sparse-Supervised Hybrid Parameterized Physics-Informed Neural Networks for Incompressible Flows Across Reynolds Numbers
por: Jangir, A., et al.
Publicado: (2026)