Exploring active learning strategies for predictive models in mechanics of materials
Author:
Funder
National Science Foundation
U.S. Department of Education
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00339-024-07728-9.pdf
Reference70 articles.
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2. Z. Zhang, K. Friedrich, Artificial neural networks applied to polymer composites: a review. Compos. Sci. Technol. 63(14), 2029–2044 (2003). https://doi.org/10.1016/S0266-3538(03)00106-4
3. H. El Kadi, Y. Al-Assaf, Prediction of the fatigue life of unidirectional glass fiber/epoxy composite laminae using different neural network paradigms. Compos. Struct. 55(2), 239–246 (2002). https://doi.org/10.1016/S0263-8223(01)00152-0
4. H.N. Bar, M.R. Bhat, C.R.L. Murthy, Identification of failure modes in gfrp using pvdf sensors: Ann approach. Compos. Struct. 65(2), 231–237 (2004). https://doi.org/10.1016/j.compstruct.2003.10.019
5. Z. Zhang, P. Klein, K. Friedrich, Dynamic mechanical properties of ptfe based short carbon fibre reinforced composites: experiment and artificial neural network prediction. Compos. Sci. Technol. 62(7–8), 1001–1009 (2002). https://doi.org/10.1016/S0266-3538(02)00036-2
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