Physics-Informed Neural Network Integrating PointNet-Based Adaptive Refinement for Investigating Crack Propagation in Industrial Applications-Reference-Cited by-同舟云学术

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Physics-Informed Neural Network Integrating PointNet-Based Adaptive Refinement for Investigating Crack Propagation in Industrial Applications

Published:2023-02 Issue:2 Volume:19 Page:2210-2218
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Tu Jingzhi¹^ORCID,Liu Chun²^ORCID,Qi Pian³

Affiliation:

1. School of Engineering and Technolgy, China University of Geosciences, Beijing, China

2. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China

3. Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Naples, Italy

Funder

Fundamental Research Funds for China Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/9989328/09868162.pdf?arnumber=9868162

Reference30 articles.

1. An Overview on Meshfree Methods: For Computational Solid Mechanics

2. A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches

3. Adaptive fourth-order phase field analysis using deep energy minimization

4. A phase field numerical manifold method for crack propagation in quasi-brittle materials

5. Transfer learning enhanced physics informed neural network for phase-field modeling of fracture

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4. Forecasting Buoy Observations Using Physics-Informed Neural Networks;IEEE Journal of Oceanic Engineering;2024-07

5. A peridynamic-informed deep learning model for brittle damage prediction;Theoretical and Applied Fracture Mechanics;2024-06

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