Physic Informed Generative

Physics-informed generative models combine the power of generative AI with the constraints of physical laws to create realistic and physically plausible synthetic data. Current research focuses on integrating various generative architectures, such as diffusion models and variational autoencoders, with physical models to improve data generation accuracy, robustness, and interpretability across diverse applications. This approach is proving valuable in fields ranging from medical imaging and materials science to geophysical monitoring and architectural design, enabling more efficient data augmentation, improved model training, and enhanced understanding of complex systems.