  Computer vision and machine learning tools offer an exciting new way for
automatically analyzing and categorizing information from complex computer
simulations. Here we design an ensemble machine learning framework that can
independently and robustly categorize and dissect simulation data output
contents of turbulent flow patterns into distinct structure catalogues. The
segmentation is performed using an unsupervised clustering algorithm, which
segments physical structures by grouping together similar pixels in simulation
images. The accuracy and robustness of the resulting segment region boundaries
are enhanced by combining information from multiple simultaneously-evaluated
clustering operations. The stacking of object segmentation evaluations is
performed using image mask combination operations. This statistically-combined
ensemble (SCE) of different cluster masks allows us to construct cluster
reliability metrics for each pixel and for the associated segments without any
prior user input. By comparing the similarity of different cluster occurrences
in the ensemble, we can also assess the optimal number of clusters needed to
describe the data. Furthermore, by relying on ensemble-averaged spatial segment
region boundaries, the SCE method enables reconstruction of more accurate and
robust region of interest (ROI) boundaries for the different image data
clusters. We apply the SCE algorithm to 2-dimensional simulation data snapshots
of magnetically-dominated fully-kinetic turbulent plasma flows where accurate
ROI boundaries are needed for geometrical measurements of intermittent flow
structures known as current sheets.
