Buchumschlag

Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Xu, Qingsong, Shi, Yilei, Bamber, Jonathan, Tuo, Ye, Ludwig, Ralf, Zhu, Xiao Xiang
Format:	Preprint
Veröffentlicht:	2023
Schlagworte:	Machine Learning Artificial Intelligence Fluid Dynamics
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Large-scale flood modeling and forecasting with FloodCast
von: Xu, Qingsong, et al.
Veröffentlicht: (2024)

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

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

Challenges and Opportunities for Machine Learning in Fluid Mechanics
von: Mendez, M. A., et al.
Veröffentlicht: (2022)

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

Meta-PINNs: Meta-Learning Enhanced Physics-Informed Machine Learning Framework for Turbomachinery Flow Predictions under Varying Operation Conditions
von: Han, Yuling, et al.
Veröffentlicht: (2026)

Physics-Based Machine Learning Closures and Wall Models for Hypersonic Transition-Continuum Boundary Layer Predictions
von: Nair, Ashish S., et al.
Veröffentlicht: (2025)

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

Discovering Boundary Equations for Wave Breaking using Machine Learning
von: Tang, Tianning, et al.
Veröffentlicht: (2024)

Machine Learning based Ensemble Flame Regime Classification for Mesoscale Combustors based on Insights from Linear and Nonlinear Dynamic Analysis
von: Ganesh, M Ashwin, et al.
Veröffentlicht: (2026)

Physics-Guided Machine Learning for Uncertainty Quantification in Turbulence Models
von: Chu, Minghan, et al.
Veröffentlicht: (2025)

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

Machine Learning Techniques for Data Reduction of CFD Applications
von: Lee, Jaemoon, et al.
Veröffentlicht: (2024)

Oscillating Heat Transfer Prediction in Porous Structures Using Generative AI-Assisted Explainable Machine Learning
von: Zhu, Lichang, et al.
Veröffentlicht: (2025)

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

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

PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
von: Takamoto, Makoto, et al.
Veröffentlicht: (2022)

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
von: Ohana, Ruben, et al.
Veröffentlicht: (2024)

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

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

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

Non-Unique Machine Learning Mapping in Data-Driven Reynolds Averaged Turbulence Models
von: Man, Anthony, et al.
Veröffentlicht: (2023)

Intelligent Optimization of Multi-Parameter Micromixers Using a Scientific Machine Learning Framework
von: Hassanzadeh, Meraj, et al.
Veröffentlicht: (2025)

Hybrid LES/RANS for flows including separation: A new wall function using Machine Learning based on binary search trees
von: Davidson, Lars
Veröffentlicht: (2024)

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

Online Optimisation of Machine Learning Collision Models to Accelerate Direct Molecular Simulation of Rarefied Gas Flows
von: Ball, Nicholas Daultry, et al.
Veröffentlicht: (2024)

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

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

Improved Prediction of Settling Behaviour of Solid Particles through Machine Learning Analysis of Experimental Retention Time Data
von: Keren, Liron Simon, et al.
Veröffentlicht: (2023)

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

Machine-learning based flow field estimation using floating sensor locations
von: Oura, Tomoya, et al.
Veröffentlicht: (2023)

Multi-Objective Aerodynamic Optimization of Ride Height and Rake Angle in a Sedan Car Using CFD and Machine Learning
von: Kheirkhah, Mahdi, et al.
Veröffentlicht: (2025)

Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations
von: Banerjee, Paulami, et al.
Veröffentlicht: (2024)

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation
von: Xiao, Ke, et al.
Veröffentlicht: (2025)

Accelerating Transient CFD through Machine Learning-Based Flow Initialization
von: Sharpe, Peter, et al.
Veröffentlicht: (2025)

Machine Learning in Viscoelastic Fluids via Energy-Based Kernel Embedding
von: Otto, Samuel E., et al.
Veröffentlicht: (2024)

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

Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning
von: Williams, Chelsea A., et al.
Veröffentlicht: (2024)

Machine learning-based prediction of species mass fraction and flame characteristics in partially premixed turbulent jet flame
von: Shateri, Amirali, et al.
Veröffentlicht: (2025)

Parametric Intrusive Reduced Order Models enhanced with Machine Learning Correction Terms
von: Ivagnes, Anna, et al.
Veröffentlicht: (2024)