Recent advances in Generative AI are transforming how we model, analyze, and predict complex scientific and engineering systems. Traditional modeling workflows—progressing from observational data to governing equations and then to simulation and analysis—often struggle with the high dimensionality, multi-physics coupling, and nonlinearity inherent in real-world systems. Moreover, such systems are frequently affected by both aleatory (inherent randomness) and epistemic (model-form) uncertainties, which pose significant challenges for accurate prediction and robust decision-making. To address these issues, generative models—including diffusion models, variational autoencoders, and score-based generative approaches—offer powerful new tools for learning latent structures, sampling from complex distributions and constructing surrogate or reduced-order models. These models can enable efficient modeling, even in regimes where traditional simulation is prohibitively expensive. For instance, in multiscale dynamical systems, generative models can learn to represent and sample from slow manifolds, capturing long-term behavior without requiring full-scale simulation. However, the application of generative AI in scientific domains is still constrained by challenges such as limited training data, high-fidelity modeling costs, and the need for physical interpretability and generalization. To unlock the full potential of these methods, further advances are needed in geometry-aware generation, data-efficient learning, and physics-informed model design. This MS aims to bring together leading researchers in generative AI, uncertainty quantification, and scientific machine learning to explore how next-generation generative models can accelerate and improve scientific discovery and engineering design. We welcome contributions that bridge theory and application across disciplines, demonstrating how generative models can be tailored to respect physical laws, reduce computational cost, and enhance predictive reliability in complex systems.