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Learning to fail: Predicting fracture evolution in brittle material
  models using recurrent graph convolutional neural networks

Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks

14 October 2018
Max Schwarzer
Bryce Rogan
Yadong Ruan
Zhengming Song
Diana Lee
A. Percus
V. Chau
B. Moore
E. Rougier
Hari S. Viswanathan
G. Srinivasan
    AI4CE
ArXivPDFHTML

Papers citing "Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks"

4 / 4 papers shown
Title
Materials Property Prediction with Uncertainty Quantification: A
  Benchmark Study
Materials Property Prediction with Uncertainty Quantification: A Benchmark Study
Daniel Varivoda
Rongzhi Dong
Sadman Sadeed Omee
Jianjun Hu
AI4CE
28
20
0
04 Nov 2022
Applications of physics-informed scientific machine learning in
  subsurface science: A survey
Applications of physics-informed scientific machine learning in subsurface science: A survey
A. Sun
H. Yoon
C. Shih
Zhi Zhong
AI4CE
29
9
0
10 Apr 2021
A Data-Driven Approach to Full-Field Damage and Failure Pattern
  Prediction in Microstructure-Dependent Composites using Deep Learning
A Data-Driven Approach to Full-Field Damage and Failure Pattern Prediction in Microstructure-Dependent Composites using Deep Learning
R. Sepasdar
Anuj Karpatne
Maryam Shakiba
AI4CE
29
60
0
09 Apr 2021
StressNet: Deep Learning to Predict Stress With Fracture Propagation in
  Brittle Materials
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
Yinan Wang
Diane Oyen
Weihong
Guo
A. Mehta
Cory Braker Scott
N. Panda
M. G. Fernández-Godino
G. Srinivasan
Xiaowei Yue
AI4CE
23
31
0
20 Nov 2020
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