918 Uncertainty Quantification Methods in Computational Engineering
Xiu Yang, Pacific Northwest National Laboratory
Heng Xiao, Virginia Polytechnic Institute and State University
Uncertainty Quantification has now established as an important area of computational mechanics and engineering, especially in the context of UQ for multi-scale, multi-physics science and engineering applications. UQ provides metrics to study the relationship between imprecisely prescribed model inputs and the model’s predictions, hence it is essential for assessing the predictive capability of simulations. With the development of data-driven methods, model reduction techniques, machine learning skills in recent years, the tools of UQ are becoming more powerful. These advances at the intersection of data and computational methods have created new opportunities to meet the long-standing challenge of delivering quantitative predictivity in computational mechanics and engineering. Through this minisymposium, we hope to highlight new advances in UQ methods that will lead to breakthroughs in the development of predictive models for realistic problems.