Advection Diffusion Reaction

Advection-diffusion-reaction (ADR) systems describe the interplay of transport, spreading, and transformation processes, crucial for modeling diverse phenomena from fluid flow in reservoirs to chemical reactions. Current research focuses on developing efficient and accurate computational methods, including physics-informed neural networks (PINNs) and novel graph neural network architectures (like ADR-GNNs), to solve these complex systems, often incorporating techniques like hierarchical model reduction and curriculum learning to improve performance and address challenges like noisy data and multi-scale features. These advancements are driving progress in areas such as carbon capture, subsurface modeling, and materials science by enabling faster and more accurate simulations of complex systems.