Machine learning of material behavior
نویسندگان
چکیده
Symbolic machine learning techniques can extract exible and comprehensible knowledge from empirical data of material behavior. The diversity of symbolic machine learning techniques ooers potential to match the requirements of many tasks when models of material behavior need to be created from data. We develop a series of steps for generating material behavior 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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