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DTSTART:19700308T020000
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DTSTAMP:20210402T160559Z
LOCATION:Track 11
DTSTART;TZID=America/New_York:20201113T110000
DTEND;TZID=America/New_York:20201113T112500
UID:submissions.supercomputing.org_SC20_sess229_ws_ai4s106@linklings.com
SUMMARY:Automatic Particle Trajectory Classification in Plasma Simulations
DESCRIPTION:Workshop\n\nAutomatic Particle Trajectory Classification in Pl
 asma Simulations\n\nMarkidis, Peng, Podobas, Jongsuebchoke, Bengtsson...\n
 \nNumerical simulations of plasma flows are crucial for advancing our unde
 rstanding of microscopic processes that drive the global plasma dynamics i
 n fusion devices, space, and astrophysical systems. Identifying and classi
 fying particle trajectories allows us to determine specific on-going accel
 eration mechanisms, shedding light on essential plasma processes.\n\nOur o
 verall goal is to provide a general workflow for exploring particle trajec
 tory space and automatically classifying particle trajectories from plasma
  simulations in an unsupervised manner. We combine pre-processing techniqu
 es, such as Fast Fourier Transform (FFT), with Machine Learning methods, s
 uch as Principal Component Analysis (PCA), k-means clustering algorithms, 
 and silhouette analysis. We demonstrate our workflow by classifying electr
 on trajectories during magnetic reconnection problem. Our method successfu
 lly recovers existing results from previous literature without a priori kn
 owledge of the underlying system.\n\nOur workflow can be applied to analyz
 ing particle trajectories in different phenomena, from magnetic reconnecti
 on, shocks to magnetospheric flows. The workflow has no dependence on any 
 physics model and can still identify particle trajectories and acceleratio
 n mechanisms that were not detected before.\n\nRegistration Category: Work
 shop Reg Pass
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