Abstract
Symbolic machine learning techniques can extract flexible and comprehensible knowledge from empirical data of material behaviour. The diversity of symbolic machine learning techniques offers potential to match the requirements of many tasks when models of material behaviour need to be created from data. We develop a series of steps for generating material behaviour knowledge from empirical data and exemplify some of them on several small datasets. We discuss some of the issues that govern knowledge extraction and, as a by-product, demonstrate that symbolic learning techniques are functionally superior to sub-symbolic learning for the task of comprehensible knowledge extraction.
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