Deep and Physics-Informed Neural Networks as a Substitute for Finite Element Analysis
Author:
Affiliation:
1. School of the Built Environment and Architecture, London South Bank University, United Kingdom
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3674029.3674043
Reference17 articles.
1. Thai, Huu-Tai. "Machine learning for structural engineering: A state-of-the-art review." In Structures, vol. 38, pp. 448-491. Elsevier, 2022.
2. Tapeh Arash Teymori Gharah and M. Z. Naser. "Artificial intelligence machine learning and deep learning in structural engineering: a scientometrics review of trends and best practices." Archives of Computational Methods in Engineering 30 no. 1 (2023): 115-159.
3. Liang Liang Minliang Liu Caitlin Martin and Wei Sun. "A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis." Journal of The Royal Society Interface 15 no. 138 (2018): 20170844.
4. Bolandi Hamed Xuyang Li Talal Salem Vishnu Naresh Boddeti and Nizar Lajnef. "Bridging finite element and deep learning: High-resolution stress distribution prediction in structural components." Frontiers of Structural and Civil Engineering 16 no. 11 (2022): 1365-1377.
5. Shin Seungyeon Ah-hyeon Jin Soyoung Yoo Sunghee Lee ChangGon Kim Sungpil Heo and Namwoo Kang. "Wheel impact test by deep learning: prediction of location and magnitude of maximum stress." Structural and Multidisciplinary Optimization 66 no. 1 (2023): 24.
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