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Abstract: Advanced manufacturing enables rapid fabrication of complex materials and structures, allowing exploration of large design spaces in materials composition, microstructure, and processing conditions. However, the high dimensionality of these spaces, together with manufacturing uncertainties and defects, poses major challenges for traditional computational science and engineering methods. Addressing these challenges requires new approaches that integrate multi-modal data with physics-based modeling and artificial intelligence to accelerate scientific discovery and materials innovation. This seminar introduces Scientific Artificial Intelligence (Sci-AI) approaches for discovering materials constitutive laws, modeling complex materials behavior, and designing new materials with targeted properties. The central theme is the tight coupling of physical principles, data-driven learning, and interpretability, enabling AI models to move beyond black-box prediction toward reliable scientific inference. The talk is organized around three interconnected themes. First, a hierarchical symbolic AI framework is presented for the automated discovery of physically interpretable constitutive laws directly from data, allowing the model to identify governing mechanisms, enforce dimensional consistency and physical constraints, and balance accuracy with model complexity. Second, a physics-informed, image-based encoder–decoder architecture is introduced to accelerate materials behavior modeling by learning compact latent representations of complex microstructural and field data, while seamlessly fusing high-fidelity simulations, in-situ imaging, and external sensing information across multiple length and time scales. Third, a generative AI framework is developed for inverse materials design, enabling the synthesis of physically plausible microstructures conditioned on nonlinear material properties, processing constraints, and performance targets, thereby closing the loop from data and modeling to design and manufacturing. Overall, this seminar highlights recent advances in Sci-AI for computational science and engineering and demonstrates how physics-guided machine learning can bridge data and models to enable advanced materials modeling and design.