Application of artificial intelligence and machine learning in peridynamics

Cong Tien Nguyen, Selda Oterkus, Erkan Oterkus

Research output: Chapter in Book/Report/Conference proceedingChapter


With the advancement of sensor technologies, data analysis, and computational resources, data-driven models have become important in many different scientific fields. However, for complex problems without having sufficient number of data, the accuracy of the data-driven approaches deteriorates. As an alternative, theory-guided data science which is a combination of physics-driven and data-driven models is a promising approach. In this chapter, a peridynamic machine learning approach is presented based on linear regression. To demonstrate the capability of the coupled peridynamic machine learning approach, four different numerical examples are considered including one-dimensional bar subjected to axial loading, vibration of a one-dimensional bar, two-dimensional plate subjected to tension loading and two-dimensional plate with a pre-existing crack subjected to tension loading.
Original languageEnglish
Title of host publicationPeridynamic Modeling, Numerical Techniques, and Applications
EditorsErkan Oterkus, Selda Oterkus, Erdogan Madenci
Place of PublicationAmsterdam, Netherlands
Number of pages17
Publication statusPublished - 30 Apr 2021

Publication series

NameA volume in Elsevier Series in Mechanics of Advanced Materials


  • artificial intelligence
  • linear regression
  • machine learning
  • nonlocal
  • peridynamics


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