Artificial intelligence and machine learning are increasingly shaping how engineers analyze, design, and assess structures. In structural engineering, these tools enable optimization, rapid surrogate modeling, and decision-making under complex loading and environmental conditions. As simulations become more automated and data-driven, ensuring their reliability and robustness becomes increasingly important. This talk discusses how computational mathematics supports trustworthy structural simulations in the AI era. Key topics include verification and validation, uncertainty quantification, and data quality, framed within the broader context of simulation governance. The focus is on how these ideas influence practical modeling choices and interpretation of results in engineering applications. The presentation will highlight examples from advanced finite element analysis and AI-enabled structural modeling, including applications to structural optimization and reliability assessment under extreme snow loads. These examples illustrate how physics-based methods and data-driven tools can be combined effectively, while emphasizing the importance of uncertainty awareness when simulations inform safety-critical decisions.
