Data Driven Generative

Data-driven generative modeling focuses on creating realistic synthetic data using machine learning, aiming to overcome limitations of traditional modeling approaches by learning patterns from existing datasets. Current research emphasizes the development and application of generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models across diverse domains, including information retrieval, biological system modeling, and sound field reconstruction. These advancements enable improved data augmentation, more accurate simulations, and the generation of novel designs, impacting fields ranging from medical imaging and drug discovery to architectural design and engineering.