Physic Informed Diffusion

Physics-informed diffusion models combine the power of generative diffusion models with the constraints of physical laws to synthesize realistic data across various scientific domains. Current research focuses on applying these models to generate diverse data types, including medical images (MRI, ultrasound), infrared images, and fluid dynamics simulations, often leveraging denoising diffusion probabilistic models and incorporating physical constraints during training. This approach addresses challenges like data scarcity and improves the accuracy and plausibility of generated data, impacting fields ranging from medical imaging and materials science to robotics and computational fluid dynamics.