Organizer(s) / Affiliation(s): Lori Graham-Brady, Johns Hopkins University
Abstract: The continued growth in computational resources and capabilities have enabled models with finer resolution and greater complexity than ever before. However, uncertainties reside throughout many of these models, such as: 1) available experimental data are often insufficient to accurately characterize model parameters, leading to parametric uncertainty; 2) material architecture is inherently random with significant differences occurring between samples of nominally identical materials; 3) uncertainties at individual scales lead to non-deterministic linkages between scales; 4) loading conditions are often highly random; and, 5) modeling errors associated with the required idealizing assumptions of the model lead to uncertain inaccuracies. This minisymposium will gather researchers who are addressing uncertainties in various stages of computational modeling and prediction for complex mechanics problems. In particular, areas of interest could include: 1. Stochastic multi-scale mechanics 2. Probabilistic prediction of fatigue, fracture, and damage 3. Homogenization techniques for random media 4. Experimental data assimilation/inverse analysis 5. Uncertainty propagation across multiple length and time scales 6. Nondeterministic computational models 7. Verification and validation techniques 8. Uncertainty quantification 9. Order reduction for complex stochastic models 10. Optimization and design under uncertainty 11. Data acquisition and stochastic modeling 12. Stochastic boundary value problems.