Fall 2019 Postdoc Seminar Series
September 25, 2019 –
Talk Title: Analyzing Collective Motion with Machine Learning and Topology
Speaker: John Nardini, Second-Year SAMSI Postdoctoral Fellow
We use topological data analysis and machine learning to study a seminal model of collective motion in biology. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based in topology that summarize the time-varying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the traditional one.
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