Process, Pattern, Prediction: Understanding Complexity in Driven Dynamical Systems
John Rundle presents Process, Pattern, Prediction: Understanding Complexity in Driven Dynamical Systems
| What | Seminar |
|---|---|
| When |
2005-05-19 04:00 PM
2005-05-19 05:00 PM
May 19, 2005 from 04:00 pm to 05:00 pm |
| Where | PES 3001 |
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John Rundle, Director, Center for Computational Science and Engineering will lead a talk titled Process, Pattern, Prediction: Understanding Complexity in Driven Dynamical Systems. All are invited to attend.
Edward N Lorenz discovered that chaos and unpredictability are hallmarks of even simple driven systems. Predicting the future evolution of a variety of driven nonlinear systems is further complicated by the fact that their dynamical processes are 1) often not amenable to direct observation; and 2) are strongly multi-scale, so that length and time scales range from very much smaller and shorter than human perception, to very much larger and longer. An example of such systems is the atmosphere, in which, from a practical standpoint, it is impossible to measure the temperatures, pressures, and humidity at all locations at all times. Other important systems include neural networks and earthquake fault systems, both of which are examples of driven threshold systems. In systems such as these, we can only observe the space-time patterns of extreme events. Using these space-time patterns, and whatever is known about the dynamics of these high-dimensional nonlinear earth systems, it often possible to construct numerical simulations that can be used to make predictions about the future space-time evolution of the system and the possible occurrence of extreme events. The accuracy of these predictions and forecasts is limited by the proximity and similarity of the model trajectory through state space, to that of the actual system. The existence of flexible new Grid computing techniques made possible by the World Wide Web has opened new avenues for the realization of sophisticated, state-of-the-art numerical simulations. Thus our ability to forecast the extreme events of the future is limited by a range of issues originating from the dynamical process of interest, the space-time patterns we can observe, and the accuracy of the predictions that are desired.