Using convolutional neural networks to predict composite properties beyond the elastic limit-Reference-Cited by-同舟云学术

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Using convolutional neural networks to predict composite properties beyond the elastic limit

Published:2019-06 Issue:2 Volume:9 Page:609-617
ISSN:2159-6859
Container-title:MRS Communications
language:en
Short-container-title:MRS Communications

Author:

Yang Charles,Kim Youngsoo,Ryu Seunghwa,Gu Grace X.

Funder

NSF Extreme Science and Engineering Discovery Environment (XSEDE)

National Research Foundation of Korea

Publisher

Springer Science and Business Media LLC

Subject

General Materials Science

Link

http://link.springer.com/content/pdf/10.1557/mrc.2019.49.pdf

Reference35 articles.

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2. U.G.K. Wegst, H. Bai, E. Saiz, A.P. Tomsia, and R.O. Ritchie: Bioinspired structural materials. Nat. Mater. 14, 23 (2015).

3. H.D. Espinosa, J.E. Rim, F. Barthelat, and M.J. Buehler: Merger of structure and material in nacre and bone—perspectives on de novo biomimetic materials. Prog. Mater. Sci. 54, 1059 (2009).

4. M.A. Meyers, J. McKittrick, and P-Y. Chen: Structural biological materials: critical mechanics-materials connections. Science 339, 773 (2013).

5. X. Wei, M. Naraghi, and H.D. Espinosa: Optimal length scales emerging from shear load transfer in natural materials: application to carbon-based nanocomposite design. ACS Nano 6, 2333 (2012).

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