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Wind Energy Engineering

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Wind Energy Engineering

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Research Frontiers in Machine Learning for Wind Resource Assessment

Development of artificial intelligence algorithms for predicting wind speed, direction, and energy potential from meteorological data and satellite imagery.

Atmospheric Turbulence Prediction via Deep Spatiotemporal Learning

Machine Learning for Microscale Wind Resource Heterogeneity Mapping

Hybrid Physics-Informed Neural Networks in Wind Potential Forecasting

Lidar Data Fusion and Anomaly Detection in Resource Assessment

Transfer Learning Across Climatically Dissimilar Wind Regimes

Uncertainty Quantification in Machine-Learned Wind Speed Models

Graph Neural Networks for Regional Wind Energy Interconnectivity

Real-Time Wind Shear Estimation from Sparse Sensor Networks

Generative Models for Synthetic Wind Climate Generation

Multi-Modal Satellite-Mesomodel Fusion for Nowcasting Capacity

All Wind Energy Engineering PhD categories