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A Self-Learning Data-Driven Development of Failure Criteria of Unknown Anisotropic Ductile Materials with Deep Learning Neural Network

Kyungsuk Jang1, Gun Jin Yun2,*

1 Department of Aerospace Engineering, Seoul National University, Seoul, 088262, South Korea
2 Institute of Advanced Aerospace Technology, Seoul National University, Seoul, 08826, South Korea

* Corresponding Author: Gun Jin Yun. Email:

Computers, Materials & Continua 2021, 66(2), 1091-1120.


This paper first proposes a new self-learning data-driven methodology that can develop the failure criteria of unknown anisotropic ductile materials from the minimal number of experimental tests. Establishing failure criteria of anisotropic ductile materials requires time-consuming tests and manual data evaluation. The proposed method can overcome such practical challenges. The methodology is formalized by combining four ideas: 1) The deep learning neural network (DLNN)-based material constitutive model, 2) Self-learning inverse finite element (SELIFE) simulation, 3) Algorithmic identification of failure points from the self-learned stress-strain curves and 4) Derivation of the failure criteria through symbolic regression of the genetic programming. Stress update and the algorithmic tangent operator were formulated in terms of DLNN parameters for nonlinear finite element analysis. Then, the SELIFE simulation algorithm gradually makes the DLNN model learn highly complex multi-axial stress and strain relationships, being guided by the experimental boundary measurements. Following the failure point identification, a self-learning data-driven failure criteria are eventually developed with the help of a reliable symbolic regression algorithm. The methodology and the self-learning data-driven failure criteria were verified by comparing with a reference failure criteria and simulating with different materials orientations, respectively.


Cite This Article

K. Jang and G. Jin Yun, "A self-learning data-driven development of failure criteria of unknown anisotropic ductile materials with deep learning neural network," Computers, Materials & Continua, vol. 66, no.2, pp. 1091–1120, 2021.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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