Mohamad H. Kazma

Large-scale dynamic systems model a wide range of systems with great societal relevance. This includes the internet, power grids, water systems, river networks, and transportation systems. Interestingly, these systems share so much in common—their mathematical models are nearly identical, they are all nonlinear and high-dimensional, and are riddled with malicious or benign uncertainty. At its core this field can be segmented into four branches: (i) physics-based modeling; (ii) improved sensing and system monitoring; (iii) realtime regulation and control; and (iv) network analysis, design, resource allocation, and uncertainty propagation. This dissertation starts with utilizing well-known, nonlinear models of various systems—models developed and calibrated for decades in power, water, and combustion networks—and presents contributions in branches (ii)–(iv). First, it revisits two classical problems in transmission power networks and water quality: where to place sensors to maximize information gain, under more realistic models with significant uncertainty. The second contribution introduces new ways to assess observability in large-scale systems. The third studies uncertainty propagation in power networks at scale. The final contribution designs partitioning algorithms to dissect networks into smaller ones, enabling localized analysis without resorting to full system methods.