Calendar

Posted November 26, 2025
Last modified January 29, 2026

Control and Optimization Seminar Questions or comments?

Anthony Bloch, University of Michigan AMS, IEEE, and SIAM Fellow
Control, Stability and Learning on Dynamic Networks

In this talk we consider various aspects of dynamics, control and learning on graphs. We discuss diffusively coupled network dynamical systems and the role of coupling in stabilizing and destabilizing such systems. We also discuss dynamic networks of this type and in particular Lyapunov-based methods for analyzing the stability of networks undergoing switching. In addition we analyze the problem of learning the dynamics of switched systems from data, including linear and polynomial systems and systems on graphs. In addition we consider the control and dynamics of systems on hypergraphs which have applications to biological networks.

Posted February 8, 2026

Combinatorics Seminar Questions or comments?

Yiwei Ge, Louisiana State University
Extremal connectivity in graphs

A $k$-connected graph is minimally (respectively, critically) $k$-connected if the deletion of any edge (respectively, vertex) results in a graph that is not $k$-connected. A graph is uniformly $k$-connected if there are exactly $k$ internally disjoint paths between every pair of vertices. These classes have played a prominent role in graph connectivity theory. We introduce super-minimally $k$-connected graphs, defined as $k$-connected graphs with no proper $k$-connected subgraph. In this talk, we will give a brief introduction to these connectivity classes, with particular emphasis on extremal problems for $3$-connected graphs.

Posted January 28, 2026

Geometry and Topology Seminar Seminar website

Nilangshu Bhattacharyya, Louisiana State University
TBA

Posted December 7, 2025
Last modified December 28, 2025

Control and Optimization Seminar Questions or comments?

Richard Vinter, Imperial College London IEEE Fellow
Control of Lumped-Distributed Control Systems

Lumped-distributed control systems are collections of interacting sub-systems, some of which have finite dimensional vector state spaces (comprising ‘lumped’ components) and some of which have infinite dimensional vector state spaces (comprising ‘distributed’ components). Lumped-distributed control systems are encountered, for example, in models of thermal or distributed mechanical devices under boundary control, when we take the control actuator dynamics or certain kinds of dynamic loading effects into account. This talk will focus on an important class of (possibly non-linear) lumped-distributed control systems, in which the control action directly affects only the lumped subsystems and the output is a function of the lumped state variables alone. We will give examples of such systems, including a temperature-controlled test bed for measuring semiconductor material properties under changing temperature conditions and robot arms with flexible links. A key observation is an exact representation of the mapping from control inputs to outputs, in terms of a finite dimensional control system with memory. (We call it the reduced system representation.) The reduced system representation can be seen as a time-domain analogue of frequency response descriptions involving the transfer function from input to output. In contrast to frequency response descriptions, the reduced system representation allows non-linear dynamics, hard constraints on controls and outputs, and non-zero initial data. We report recent case studies illustrating the computational advantages of the reduced system representation. We show that, for related output tracking problems, computation methods based on the new representation offer significantly improved tracking and reduction in computation time, as compared with traditional methods, based on the approximation of infinite dimensional state spaces by high dimensional linear subspaces.