Diffusion Field

Diffusion fields represent a powerful approach to modeling the distribution of continuous functions across various spaces, enabling the generation and analysis of complex data like images, videos, and 3D structures. Current research focuses on developing and refining diffusion probabilistic field (DPF) models, often incorporating neural networks like graph neural networks (GNNs) or neural ordinary differential equations (NODEs), to improve scalability, accuracy, and the ability to handle diverse data modalities and non-Euclidean spaces. These advancements are significantly impacting fields such as material science (e.g., crystal structure prediction), robotics (e.g., grasp and motion optimization), and medical imaging (e.g., anomaly detection in perfusion imaging), by providing robust generative and analytical tools for complex systems. The incorporation of uncertainty quantification and physics-informed constraints further enhances the reliability and applicability of these models.