Event Details

Talks on Data from Dynamic Systems

Speakers and Titles: Professor Peter Young – “Data-Based Mechanistic Modelling, Forecasting and Control”, Chris Arridge – “Dynamical Behaviour in Space Environments” and Nick Chappell & Wlodek Tych will be presenting “Modelling dynamics? And which dynamics is that? Aspects of temporal scales in modelling hydro-meteorological processes in a river catchment”.

These talks should be of interest to anyone researching in time series, simulations, dynamical systems or control theory.

Joining: via Teams.

Chris Arridge

Dynamical Behaviour in Space Environments

The space environment around a planet is a dynamic region known as a magnetosphere and a major thrust of research is to understand the origin and evolution of dynamics in these systems. At an elemental level these systems can be conceived of as set of scalar (e.g., plasma density), vector (e.g., electric and magnetic fields) and tensor fields (e.g., plasma pressure) that are non-linearly coupled to each other through the fundamental equations of physics. Although one might conceive of this as a neat self-contained system, these fields have boundary conditions that couple the system to the Sun and the planet they surround, and can be strongly driven by these boundary conditions. A major driver of research in the field is determining the dynamical behaviours and drivers of these behaviours and is often approached by analysing time series data from measurements of the fields at a few (<5) points in space. In this talk I outline this problem in more detail and discuss two specific examples where we try to retrieve the hidden variables characterising system coupling and driving dynamics, one using the Kalman filter and one using Approximate Bayesian Computation.

Nick Chappell and Wlodek Tych

Modelling dynamics? And which dynamics is that?

Aspects of temporal scales in modelling hydro-meteorological processes in a river catchment

We highlight the broad temporal spectrum of catchment processes and of those driving the water cycle along with the main difficulties in building effective catchment models. Several examples illustrate the narrative leading towards the need for frequent sampling of hydrological processes as well as for a pragmatic approach to their modelling, highlighting the use of principle of parsimony and the effectiveness of simple pragmatic models addressing a specific aim or a research question.