PiGRAND: Physics-informed Graph Neural Diffusion for Intelligent Additive Manufacturing

ArXi:2603.15194v1 Announce Type: new A comprehensive understanding of heat transport is essential for optimizing various mechanical and engineering applications, including 3D printing. Recent advances in machine learning, combined with physics-based models, have enabled a powerful fusion of numerical methods and data-driven algorithms. This progress is driven by the availability of limited sensor data in various engineering and scientific domains, where the cost of data collection and the inaccessibility of certain measurements are high.