Surface Flux

Surface flux research focuses on quantifying and modeling the flow of various quantities across a surface, ranging from magnetic fields on the sun to greenhouse gases on Earth and even ship traffic patterns. Current research employs machine learning techniques, including physics-informed neural networks, variational methods, and deep learning frameworks like transformers, to improve the accuracy and efficiency of flux estimations and predictions. These advancements are crucial for improving climate models, assessing environmental risks (e.g., invasive species spread), and optimizing resource management in diverse fields like solar physics and urban planning. The development of more accurate and computationally efficient models is driving significant progress in these areas.