Approximating Fracture Paths in Random Heterogeneous Materials: A Probabilistic Learning Perspective-Reference-Cited by-同舟云学术

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Approximating Fracture Paths in Random Heterogeneous Materials: A Probabilistic Learning Perspective

Published:2024-08 Issue:8 Volume:150 Page:
ISSN:0733-9399
Container-title:Journal of Engineering Mechanics
language:en
Short-container-title:J. Eng. Mech.

Author:

Quek Ariana¹,Yi Yong Jin²,Guilleminot Johann³

Affiliation:

1. Dept. of Mechanical Engineering and Materials Science, Duke Univ., Durham, NC 27708.

2. Dept. of Civil and Environmental Engineering, Duke Univ., Durham, NC 27708.

3. Professor, Dept. of Civil and Environmental Engineering, Duke Univ., Durham, NC 27708 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JENMDT.EMENG-7617

Reference78 articles.

1. Solving ill-posed inverse problems using iterative deep neural networks

2. A review on phase-field models of brittle fracture and a new fast hybrid formulation;Ambati M.;Comput. Mech.,2014

3. Approximation of functional depending on jumps by elliptic functional via t-convergence

4. Batlle P. M. Darcy B. Hosseini and H. Owhadi. 2023. “Kernel methods are competitive for operator learning.” Preprint submitted April 26 2023. https://arxiv.org/abs/1504.06600v1.

5. Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Heteroscedastic Gaussian Process Regression for material structure–property relationship modeling;Computer Methods in Applied Mechanics and Engineering;2024-11

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