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Design and Optimization of Cementitious Systems with Machine Learning

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thesis
posted on 14.05.2021, 20:02 authored by Christopher ChildsChristopher Childs
Machine learning has revolutionized disciplines within materials science that have been able to generate sufficiently large datasets to utilize algorithms based on statistical inference, but for many important classes of materials the datasets remain small. After an introduction to various types of ML regression, Chapter 1 introduces a rapidly growing number of approaches to show how embedding domain knowledge of materials systems are reducing data requirements and allowing broader applications of machine learning. Furthermore, these hybrid approaches improve the interpretability of the predictions, allowing for greater physical insight into the factors that
determine material properties. A survey of the modern utilization of machine learning for cementitious systems is discussed.

History

Date

19/01/2021

Degree Type

Dissertation

Department

Chemistry

Degree Name

  • Doctor of Philosophy (PhD)

Advisor(s)

Newell Washburn

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