Colloquium followed by refreshments in Monadelphous Integrated Learning Centre
Applied mathematics involves building models of the real world. When those models consist of differential equations (as they very often do) one can seek to do one of two things: (1) make approximations that allow those models to be solved on a restricted, but still useful, domain; or (2) study those equations and offer a descriptive understanding of the dynamics of the underlying system. Increasingly, our ability to collect data is exceeding our skill at constructing viable and interesting systems of equations. I will describe the situation where we have a stream of observed time series data but insufficient expertise to write down an appropriate generating set of equations. From that stream of data, I will describe methods that we have been working on that offers a descriptive understanding of the dynamics that is equivalent to what might have been obtained from the equations. That is, for chaotic systems, we seek to estimate quantities such as Lyapunov exponents, (unstable) periodic orbits, symbolic dynamics, correlation dimension, and attractor topology directly from the data. This allows us to describe the behaviour of the underlying system from observed data, and importantly for practical applications quantify change in that behaviour.
Applied mathematics involves building models of the real world. When those models consist of differential equations (as they very often do) one can seek to do one of two things: (1) make approximations that allow those models to be solved on a restricted, but still useful, domain; or (2) study those equations and offer a descriptive understanding of the dynamics of the underlying system. Increasingly, our ability to collect data is exceeding our skill at constructing viable and interesting systems of equations. I will describe the situation where we have a stream of observed time series data but insufficient expertise to write down an appropriate generating set of equations. From that stream of data, I will describe methods that we have been working on that offers a descriptive understanding of the dynamics that is equivalent to what might have been obtained from the equations. That is, for chaotic systems, we seek to estimate quantities such as Lyapunov exponents, (unstable) periodic orbits, symbolic dynamics, correlation dimension, and attractor topology directly from the data. This allows us to describe the behaviour of the underlying system from observed data, and importantly for practical applications quantify change in that behaviour.