Robust deep learning on graphs using neural PDEs - Gui, Pengzhe (Nanyang Technological University, 2023)
Neural Partial Differential Equations(Neural PDEs) offer a data-driven method for modeling and solving high-dimensional PDE problems by combining the robust representation capabilities of deep learning with the traditional framework of partial differential equations (PDEs). This approach combines the strengths of machine learning, mathematical modelling, and numerical methods in order to gain a deeper understanding of the underlying processes and make accurate predictions.