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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title Extracting Symbolic Relationships in Materials Science and Engineering
Author(s) Evgeniya Kabliman
On-Site Speaker (Planned) Evgeniya Kabliman
Abstract Scope Symbolic relationships are essential for science. They allow a straightforward analysis of dependencies between material properties and influencing parameters. When it comes to materials science and engineering, understanding the underlying process-structure-property relationships is crucial for optimizing existing materials and processes, as well as designing completely new ones. This lecture will explore how symbolic regression can extract the symbolic relationships from material and process data. In particular, the mechanical testing of metallic alloys will be considered, and the extraction of the constitutive laws will be demonstrated. Symbolic regression can automatically discover interpretable mathematical equations by simultaneously finding the equation's form and parameters, thus offering unique opportunities when uncovering hidden correlations.
Proceedings Inclusion? Undecided

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