Analyzing collective motion with machine learning and topology
| Autor: | Angelika Manhart, Kathleen M. Storey, Chad M. Topaz, Lori Ziegelmeier, Jesse Milzman, John T. Nardini, Dhananjay Bhaskar |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Physical sciences
General Physics and Astronomy Model parameters Pattern Formation and Solitons (nlin.PS) Machine learning computer.software_genre Topology 01 natural sciences 010305 fluids & plasmas 0103 physical sciences FOS: Mathematics Algebraic Topology (math.AT) 55U10 Mathematics - Algebraic Topology Physics - Biological Physics 010306 general physics Mathematical Physics Mathematics Persistent homology business.industry Flocking (behavior) Applied Mathematics Collective motion Statistical and Nonlinear Physics Nonlinear Sciences - Pattern Formation and Solitons Biological Physics (physics.bio-ph) Social force Topological data analysis Artificial intelligence business computer Regular Articles |
| Zdroj: | Chaos |
| ISSN: | 1089-7682 |
| Popis: | We use topological data analysis and machine learning to study a seminal model of collective motion in biology [D'Orsogna et al., Phys. Rev. Lett. 96 (2006)]. 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 one that is based on traditional order parameters. Comment: Published in Chaos 29, 123125 (2019), DOI: 10.1063/1.5125493 |
| Databáze: | OpenAIRE |
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