# Complex dynamical systems

Tuesday, October 6, 2015 - 1:25pm - 2:25pm

Mihailo Jovanovic (University of Minnesota, Twin Cities)

We study the problem of completing partially known state statistics of

complex dynamical systems using low-complexity linearized models. State

statistics of linear systems satisfy certain structural constraints that

arise from the underlying dynamics and the directionality of input

disturbances. The dynamical interaction between state variables is known

while the directionality of input excitation is uncertain. Thus, the goal

of the inverse problem that we formulate is to identify the dynamics and

complex dynamical systems using low-complexity linearized models. State

statistics of linear systems satisfy certain structural constraints that

arise from the underlying dynamics and the directionality of input

disturbances. The dynamical interaction between state variables is known

while the directionality of input excitation is uncertain. Thus, the goal

of the inverse problem that we formulate is to identify the dynamics and

Wednesday, November 9, 2005 - 11:00am - 12:00pm

John Sidles (University of Washington)

Complex systems are ubiquitous in mathematics, biology,

engineering, and

physics, and the past ten years have witnessed an exponential

increase in

the literature associated such systems. A shared conceptual

framework is

becoming apparent among challenges as seemingly different as the

following:

the search by mathematicians for exact high-order trigonometric

identities,

the search by engineers for stable control systems, the search by

engineering, and

physics, and the past ten years have witnessed an exponential

increase in

the literature associated such systems. A shared conceptual

framework is

becoming apparent among challenges as seemingly different as the

following:

the search by mathematicians for exact high-order trigonometric

identities,

the search by engineers for stable control systems, the search by