Advection Diffusion Process

Advection-diffusion processes describe the transport of quantities like heat, fluids, or information, combining directional movement (advection) with spreading due to random motion (diffusion). Current research focuses on improving the accuracy and efficiency of modeling these processes, particularly using machine learning techniques such as graph neural networks (GNNs) and physics-informed neural networks (PINNs), often decomposing the process into advection and diffusion components for better understanding and prediction. These advancements have significant implications for diverse fields, including weather forecasting, medical imaging analysis, and the development of more robust and efficient computational models for complex systems.