Upcoming Events

Prof. Stephanie White
Long Island University
Brookville, New York, USA

Title: Systems Theory, Systems Thinking

Thursday, November 15, 2018, 2:00 pm to 3:00 pm
Room 4018, Fred Kaiser Building, 2332 Main Mall, University of British Columbia, Vancouver, BC, Canada V6T 1Z4

Light refreshments will be served.
The event is open to public.
We would greatly appreciate if you would please register so that we may more accurately estimate the room size and refreshments.
Map: Kaiser Building
Map: Kaiser Building on Google maps

This talk discusses several important concepts in cybernetics, including organized complexity, transformation, feedback, and control. The talk concludes with a brief overview of three important systems thinking approaches: Jay Forrester’s System Dynamics, Peter Checkland’s Soft Systems Methodology (SSM), and Stafford Beer’s Viable Systems Model (VSM). Using causal loops and System Dynamics, the analyst can model and simulate interactions among system elements and avoid emergent problems when a system or organization becomes operational. Using SSM, the analyst learns about the whole problem under study, the criteria for success, and all the influences and constraints. Using VSM the analyst diagnoses and controls the organization and works towards improving it.

Dr. Stephanie White is a Senior Professor at LIU-Post. She teaches a doctoral course in systems theory and undergraduate and master’s level courses in computer science and systems engineering. Previously Stephanie was Principal Engineer of requirements and architecture for the Northrop Grumman Advanced Technology and Development Center in Bethpage. During her twenty-year career at Grumman and Northrop Grumman, she was a Principal Investigator on government-funded research contracts and transferred technology to aircraft and space programs. Dr. White is President of the IEEE Systems Council, serves on the IEEE Technical Activities Board, and Chairs the IEEE Thesaurus Editorial Board. She received an IEEE-USA Award for inspiring women to work in STEM fields and for leadership in diversity initiatives.


Prof. Francesco Sorrentino
Department of Mechanical Engineering
University of New Mexico

Title: Optimal Control of Networks: Energy Scaling and Open Challenges
Friday, November 23, 2018, 2:00 pm to 3:00 pm
Visualization Lab, ASB 10940 (SFU’s Big Data Hub), Simon Fraser University, Burnaby, BC, Canada

Light refreshments will be served.
The event is open to public.
We would greatly appreciate if you would please register so that we may more accurately estimate the room size and refreshments.
Map: SFU

Recent years have witnessed increased interest from the scientific community regarding the control of complex dynamical networks. Some common types of networks examined throughout the literature are power grids, communication networks, gene regulatory networks, neuronal systems, food webs, and social systems. Optimal control studies strategies to control a system that minimize a cost function, for example the energy that is required by the control action.

We show that by controlling the states of a subset of the nodes of a network, rather than the state of every node, the required energy to control a portion of the network can be reduced substantially. The energy requirements exponentially decay with the number of target nodes, suggesting that large networks can be controlled by a relatively small number of inputs, as long as the target set is appropriately sized.

An important observation is that the minimum energy solution of the control problem for a linear system produces a control trajectory that is nonlocal. However, when the network dynamics is linearized, the linearization is only valid in a local region of the state space and hence the question arises whether optimal control can be used. We provide a solution to this problem by determining the region of state space where the trajectory does remain local and so minimum energy control can still be applied to linearized approximations of nonlinear systems. We apply our results to develop an algorithm that determines a piecewise open-loop control signal for nonlinear systems. Applications include controlling power grid dynamics and the regulatory dynamics of the intracellular circadian clock.

This work is in collaboration with Isaac Klickstein and Afroza Shirin (UNM).

[1] I. Klickstein, A. Shirin, F. Sorrentino, “Energy Scaling of Targeted Optimal Control of Complex Networks”, Nature Communications, 8, 15145 (2017)
[2] I. Klickstein, A. Shirin, F. Sorrentino, “Locally Optimal Control of Complex Networks”, Phys. Rev. Lett., 119, 268301 (2017)

Francesco Sorrentino received a PhD in Control Engineering from the University of Naples Federico II (Italy). He was first a postdoc and then visiting assistant professor in the Nonlinear Dynamics and Chaos Group at the University of Maryland at College Park. In 2008 he became assistant professor at the University of Napoli Parthenope. In 2012 he joined the Department of Mechanical Engineering at the University of New Mexico. His research primarily focuses on cutting-edge topics in Nonlinear Dynamics and Chaos Theory. His work includes studies on dynamics and control of complex networks, adaptive sensor networks, adaptation in complex systems, and identification of nonlinear systems. Other subjects of interest are the dynamics of large networks of coupled neurons and evolutionary game theory. His research is funded by the National Science Foundation, the Office of Naval Research, and the Defense Threat Reduction Agency.