Supervised cadre models for subpopulation-based learning
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Authors
New, Alexander
Issue Date
2019-08
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Mathematics
Alternative Title
Abstract
We consider four primary case studies. The first applies scalar regression to materials-by-design. In it, the SCM provides state-of-the-art prediction of polymer glass transition temperature. The method identifies cadres of polymers that respond differently to structural perturbations, thus providing design insight for targeting or avoiding specific transition temperature ranges. It identifies chemically meaningful polymer subpopulations.
Description
August 2019
School of Science
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY