Computational Tools for Reduced-Order Modeling of Very Large Dynamical Systems

The continual and compelling need for accurately and efficiently simulating dynamical behavior of physical systems arising from a wide variety of applications has led to increasingly large and complex models. Reduced-order modeling (ROM) techniques, also called model reduction or macromodeling, play an indispensable role in providing efficient computational prototyping tools to replace such large-scale models by approximate smaller models, which are capable of capturing critical dynamical behavior and faithfully preserving essential properties of the larger models. An accurate and effective reduced-order model can be applied for steady-state analysis, transient analysis, or sensitivity analysis of large-scale models and the physical systems they emulate. Consequently, scientists and engineers can significantly reduce design time and pursue more aggressive design strategies. Designers can try "what-if" experiments in hours instead of days.

In this project, we have conducted a broad range of synergistic research activities on reduced-order modeling of very large dynamical systems relating to these interlinking strands: theory, reliable algorithms, high-performance software, and applications. In particular, we have promoted and supported the applications of ROM techniques in single and multi-port network reductions for the simulation of large high-speed interconnect networks, and Computed-Aided Engineering (CAE) tools for structural dynamics analysis, and reducedorder dynamic macro models of MEMS.

This project also supports the SUGAR project, which is developing an efficient system-level tool for the simulation and design of complex MEMS.