Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approach
Fuente:
arXiv
Saved in:
| Main Authors: | Wold, Jacob Wulff, Stadtmann, Florian, Rasheed, Adil, Tabib, Mandar, San, Omer, Horn, Jan-Tore |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Prospects of federated machine learning in fluid dynamics
by: San, Omer, et al.
Published: (2022)
by: San, Omer, et al.
Published: (2022)
Model fusion with physics-guided machine learning
by: Pawar, Suraj, et al.
Published: (2021)
by: Pawar, Suraj, et al.
Published: (2021)
Revealing the drivers of turbulence anisotropy over flat and complex terrain: an interpretable machine learning approach
by: Mosso, Samuele, et al.
Published: (2025)
by: Mosso, Samuele, et al.
Published: (2025)
Diagnostic Digital Twin for Anomaly Detection in Floating Offshore Wind Energy
by: Stadtmann, Florian, et al.
Published: (2024)
by: Stadtmann, Florian, et al.
Published: (2024)
Superresolving Non-linear PDE Dynamics with Reduced-Order Autodifferentiable Ensemble Kalman Filtering For Turbulence Modeling and Flow Regulation
by: Dhingra, Mrigank, et al.
Published: (2025)
by: Dhingra, Mrigank, et al.
Published: (2025)
A machine-learning optimized vertical-axis wind turbine
by: Liu, Huan, et al.
Published: (2025)
by: Liu, Huan, et al.
Published: (2025)
Data Integration Framework for Virtual Reality Enabled Digital Twins
by: Stadtmann, Florian, et al.
Published: (2024)
by: Stadtmann, Florian, et al.
Published: (2024)
Digital Twin for Wind Energy: Latest updates from the NorthWind project
by: Rasheed, Adil, et al.
Published: (2024)
by: Rasheed, Adil, et al.
Published: (2024)
SIMR-NO: A Spectrally-Informed Multi-Resolution Neural Operator for Turbulent Flow Super-Resolution
by: Abid, Muhammad, et al.
Published: (2026)
by: Abid, Muhammad, et al.
Published: (2026)
Accelerating Bayesian inverse design in computational fluid dynamics using neural operators
by: Tiwari, Bipin, et al.
Published: (2026)
by: Tiwari, Bipin, et al.
Published: (2026)
Wind farm layout optimization using a novel machine learning approach
by: Anjiraki, Mehrshad Gholami, et al.
Published: (2025)
by: Anjiraki, Mehrshad Gholami, et al.
Published: (2025)
Assessment of machine learning methods for state-to-state approaches
by: Campoli, Lorenzo, et al.
Published: (2021)
by: Campoli, Lorenzo, et al.
Published: (2021)
Predictive Digital Twin for Condition Monitoring Using Thermal Imaging
by: Menges, Daniel, et al.
Published: (2024)
by: Menges, Daniel, et al.
Published: (2024)
Application of machine learning algorithm in temperature field reconstruction
by: He, Qianyu, et al.
Published: (2025)
by: He, Qianyu, et al.
Published: (2025)
Neural-ISAM: A hybrid in-situ machine learning approach for complex manifold-based combustion models in LES of turbulent flames
by: Fush, S. Trevor, et al.
Published: (2026)
by: Fush, S. Trevor, et al.
Published: (2026)
Defiltering turbulent flow fields for Lagrangian particle tracking using machine learning techniques
by: Oura, Tomoya, et al.
Published: (2024)
by: Oura, Tomoya, et al.
Published: (2024)
Super-resolution with dynamics in the loss
by: Page, Jacob
Published: (2024)
by: Page, Jacob
Published: (2024)
Single-snapshot machine learning for super-resolution of turbulence
by: Fukami, Kai, et al.
Published: (2024)
by: Fukami, Kai, et al.
Published: (2024)
Toward ultra-efficient high fidelity predictions of wind turbine wakes: Augmenting the accuracy of engineering models via LES-trained machine learning
by: Santoni, Christian, et al.
Published: (2024)
by: Santoni, Christian, et al.
Published: (2024)
The impact of observation density on Bayesian inversion of latent dynamics in shock-dominated flows
by: Tiwari, Bipin, et al.
Published: (2026)
by: Tiwari, Bipin, et al.
Published: (2026)
Turbulent dispersion of breath by the wind
by: Poydenot, Florian, et al.
Published: (2021)
by: Poydenot, Florian, et al.
Published: (2021)
Role of flow topology in wind-driven wildfire propagation
by: Viknesh, Siva, et al.
Published: (2024)
by: Viknesh, Siva, et al.
Published: (2024)
Bridging Large Eddy Simulation and Reduced Order Modeling of Convection-Dominated Flows through Spatial Filtering: Review and Perspectives
by: Quaini, Annalisa, et al.
Published: (2024)
by: Quaini, Annalisa, et al.
Published: (2024)
Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches
by: Reuter, Julia, et al.
Published: (2025)
by: Reuter, Julia, et al.
Published: (2025)
Explosively driven Richtmyer--Meshkov instability jet suppression and enhancement via coupling machine learning and additive manufacturing
by: Sterbentz, Dane M., et al.
Published: (2024)
by: Sterbentz, Dane M., et al.
Published: (2024)
Statistical machine learning tools for probabilistic closures of turbulence models
by: Lemos, Julia Domingues, et al.
Published: (2025)
by: Lemos, Julia Domingues, et al.
Published: (2025)
A new instability driven by the combined effect of wind stress and rotation in a sheared liquid layer
by: Preethi, S., et al.
Published: (2025)
by: Preethi, S., et al.
Published: (2025)
Supervised machine learning of compressible flow past a rotating cylinder
by: Kumar, Sanjeev, et al.
Published: (2026)
by: Kumar, Sanjeev, et al.
Published: (2026)
Toward ultra-efficient high-fidelity prediction of bed morphodynamics of large-scale meandering rivers using a novel LES-trained machine learning approach
by: Zhang, Zexia, et al.
Published: (2024)
by: Zhang, Zexia, et al.
Published: (2024)
Physics-informed neural networks for solving moving interface flow problems using the level set approach
by: Mullins, Mathieu, et al.
Published: (2025)
by: Mullins, Mathieu, et al.
Published: (2025)
Pseudo-2D RANS: A LiDAR-driven mid-fidelity model for simulations of wind farm flows
by: Letizia, Stefano, et al.
Published: (2021)
by: Letizia, Stefano, et al.
Published: (2021)
Prediction of turbulent energy based on low-rank resolvent modes and machine learning
by: Fan, Yitong, et al.
Published: (2024)
by: Fan, Yitong, et al.
Published: (2024)
Predicting liquid properties and behavior via droplet pinch-off and machine learning
by: Wang, Jingtao, et al.
Published: (2025)
by: Wang, Jingtao, et al.
Published: (2025)
High-efficient machine learning projection method for incompressible Navier-Stokes equations
by: Chen, Ruilin
Published: (2025)
by: Chen, Ruilin
Published: (2025)
A novel simulation approach for concentration-driven evaporation in capillaries
by: Namesnik, Phil, et al.
Published: (2025)
by: Namesnik, Phil, et al.
Published: (2025)
Data repairing and resolution enhancement using data-driven modal decomposition and deep learning
by: Hetherington, A., et al.
Published: (2024)
by: Hetherington, A., et al.
Published: (2024)
Convergent series of Stokes wave of arbitrary height in deep water via machine learning
by: Lin, Chong, et al.
Published: (2025)
by: Lin, Chong, et al.
Published: (2025)
Reduced-order modeling of a viscoelastic turbulent jet with hybrid machine learning models
by: Amor, Christian, et al.
Published: (2026)
by: Amor, Christian, et al.
Published: (2026)
Transition mechanisms in hypersonic wind-tunnel nozzles: a methodological approach using global linear stability analysis
by: Lemarquand, Hugo, et al.
Published: (2025)
by: Lemarquand, Hugo, et al.
Published: (2025)
Data-driven approach for modeling Reynolds stress tensor with invariance preservation
by: Fu, Xuepeng, et al.
Published: (2023)
by: Fu, Xuepeng, et al.
Published: (2023)
Similar Items
-
Prospects of federated machine learning in fluid dynamics
by: San, Omer, et al.
Published: (2022) -
Model fusion with physics-guided machine learning
by: Pawar, Suraj, et al.
Published: (2021) -
Revealing the drivers of turbulence anisotropy over flat and complex terrain: an interpretable machine learning approach
by: Mosso, Samuele, et al.
Published: (2025) -
Diagnostic Digital Twin for Anomaly Detection in Floating Offshore Wind Energy
by: Stadtmann, Florian, et al.
Published: (2024) -
Superresolving Non-linear PDE Dynamics with Reduced-Order Autodifferentiable Ensemble Kalman Filtering For Turbulence Modeling and Flow Regulation
by: Dhingra, Mrigank, et al.
Published: (2025)