Enhancing Wind Field Resolution in Complex Terrain through a Knowledge-Driven Machine Learning Approach

This research proposes a machine learning-driven, computationally efficient approach utilizing a modified Generative Adversarial Network to enhance wind flow resolution in complex terrain, delivering comparable accuracy to high-resolution simulations while substantially reducing computational demands.