Structured approaches to large-scale systems : Variational integrators for interconnected Lagrange- Dirac systems and structured model reduction on Lie groups

This dissertation presents two projects related to the structured integration of large-scale mechanical systems. Structured integration uses the considerable differential geometric structure inherent in mechanical motion to inform the design of numerical integration schemes. This process improves the qualitative properties of simulations and becomes especially valuable as a measure of accuracy over long time simulations in which traditional Gronwall accuracy estimates lose their meaning. Often, structured integration schemes replicate continuous symmetries and their associated conservation laws at the discrete level. Such is the case for variational integrators, which discretely replicate the process of deriving equations of motion from variational principles. This results in the conservation of momenta associated to symmetries in the discrete system and conservation of a symplectic form when applicable. In the case of Lagrange-Dirac systems, variational integrators preserve a discrete analogue of the Dirac structure preserved in the continuous flow. In the first project of this thesis, we extend Dirac variational integrators to accommodate interconnected systems. We hope this work will find use in the fields of control, where a controlled system can be thought of as a ̀̀plant" system joined to its controller, and in the approach of very large systems, where modular modeling may prove easier than monolithically modeling the entire system. The second project of the thesis considers a different approach to large systems. Given a detailed model of the full system, can we reduce it to a more computationally efficient model without losing essential geometric structures in the system? Asked without the reference to structure, this is the essential question of the field of model reduction. The answer there has been a resounding yes, with Principal Orthogonal Decomposition (POD) with snapshots rising as one of the most successful methods. Our project builds on previous work to extend POD to structured settings. In particular, we consider systems evolving on Lie groups and make use of canonical coordinates in the reduction process. We see considerable improvement in the accuracy of the reduced model over the usual structure-agnostic POD approac