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<div class="">A reminder of a message circulated earlier. </div>
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<div class="">I am very pleased to announce the next seminar in our Joining the Dots series, which connects topological data analysis, machine learning, and statistics. The talk will take place at <b class="">14:00 on Wednesday, 17 October</b>, in the <b class="">Mathematics
Building (54), lecture room 54/5025 (5B</b>), which is half floor up from level five. </div>
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<div class="">The speaker is </div>
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<div class=""><b class="">Dr Miro Kramar</b>, of INRIA Saclay, </div>
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<div class="">who is a renowned specialist in using topological data analysis to model the evolution of realistic physical systems. </div>
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<div class="">The details are as follows: </div>
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<div class=""><b class="">Towards Understanding Complex Spatio-Temporal Systems </b><br class="">
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Nonlinear dynamical systems play an important role in modelling of various processes in fields ranging from physics, chemistry and biology to many other natural and social sciences. Despite the importance of nonlinear models and intense efforts of many researchers,
the global dynamics of many of these systems is still far from being properly understood. Our comprehension of the dynamics becomes even more tentative if the governing equations are not known. In this case the study of the system is based on data collected
from experiments. In this talk I will introduce rigorous mathematical methods for analysing the dynamics of a system from time series and demonstrate these methods on a variety of different problems which exhibit an intricate pattern formation. In the first
part I will explain how to describe these patterns using persistent homology. Persistent homology allows us to transform experimental or numerical data into a point cloud in the space of persistence diagrams. There are a variety of metrics that can be imposed
on the space of persistence diagrams. By choosing different metrics one can interrogate the pattern locally or globally, which provides deeper insight into the dynamics of the process of pattern formation. In the second part of this talk I will discuss topological
methods for identifying robust dynamical structures that act as organising block of the dynamics. </div>
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<div class="">The talk will be accessible to a wide scientific audience and we warmly invite all interested to attend. </div>
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<div class="">Best wishes, </div>
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<div class="">Jacek </div>
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