SUMMARY

In this presentation, the opportunities for managing uncertainty in simulation-based design of multiscale systems are explored using constructs from information management and robust design. A multiscale system is simulated with models at multiple length and time scales. The accuracy of the simulated performance is determined by the trade-off between computational cost for model refinement and the benefits of mitigated uncertainty from the refined models. Hence, the motivating question: How should a system level designer allocate resources for auxiliary simulation model refinement while satisfying system level design objectives and ensuring robust process requirements for multiscale systems? My approach consists of integrating: (i) a robust design method for multiscale systems, and (ii) an information economics based approach for quantifying the cost-benefit trade-off for mitigating uncertainty in simulation models. Specifically, my approach focuses on allocating resources for reducing model parameter uncertainty arising due to insufficient data from simulation models. A comprehensive multiscale design problem, the concurrent design of material and product is used to demonstrate my approach. System level designers can efficiently allocate resources for sequential simulation model refinement in multiscale systems using the proposed approach.