Uncovering stress fields and defects distributions in graphene using deep neural networks

Overview of attention for article published in International Journal of Fracture, May 2023

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Among the highest-scoring outputs from this source (#26 of 173)
Good Attention Score compared to outputs of the same age (71st percentile)

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twitter: 5 X users

Citations

dimensions_citation: 3 Dimensions

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mendeley: 7 Mendeley

Summary X Dimensions citations

So far, Dimensions has found 3 publications that cite this research output.

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The most recent citing publications are shown below. View all 3 publications that cite this research output on Dimensions.

Prediction of microstructural-dependent mechanical properties, progressive damage, and stress distribution from X-ray computed tomography scans using a deep learning workflow

Article in Computer Methods in Applied Mechanics and Engineering (May 2024)

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

Article in Machine Learning: Science and Technology (March 2024)

Integrating uncertainty into deep learning models for enhanced prediction of nanocomposite materials’ mechanical properties

Article in APL Machine Learning (February 2024)

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