This workshop brings together scholars from mathematics, statistics, computational physics, science, and engineering to discuss the foundations, advancements, and future directions of generative modeling. Key topics include:

• Techniques: Normalizing flows, score-based diffusion models, and more.
• Foundations: Dynamical systems, probability flow, optimal transport, and additional concepts.
• Mathematical Understanding: Development of models and training algorithms.
• Applications: Sampling problems, inverse problems, design space search, AI for science, etc.

The workshop aims to provide a unique platform for experts across various fields to explore both the foundations and potential of generative AI. Participants will discuss the capabilities and limitations of current generative models, fostering collaboration on broad applications and theoretical depth from mathematical and scientific perspectives. Given the diversity of this topic, tutorial-level lectures and presentations are particularly welcome to encourage cross-disciplinary research and nurture junior researchers.