Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Applied Machine Learning to Predict Stress Hotspots II: Hexagonal close
  packed materials   Stress hotspots are regions of stress concentrations that form under
deformation in polycrystalline materials. We use a machine learning approach to
study the effect of preferred slip systems and microstructural features that
reflect local crystallography, geometry, and connectivity on stress hotspot
formation in hexagonal close packed materials under uniaxial tensile stress. We
consider two cases: one without any preferred slip systems with a critically
resolved shear stress (CRSS) ratio of 1:1:1, and a second with CRSS ratio
0.1:1:3 for basal: prismatic: pyramidal slip systems. Random forest based
machine learning models predict hotspot formation with an AUC (area under
curve) score of 0.82 for the Equal CRSS and 0.81 for the Unequal CRSS cases.
The results show how data driven techniques can be utilized to predict hotspots
as well as pinpoint the microstructural features causing stress hotspot
formation in polycrystalline microstructures

16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
1
The categories of this paper are: cs
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom