Abstract
The finite element method (FEM) is a well-known method for numerically solving partial differential equations (PDEs) over a physical domain. It has been applied successfully to solve various problems in the field of structural analysis, electromagnetics, heat transfer, fluid flows, etc. However, the issue of improving FEM has been going on for the last 50 years. The objective of the study is to create an artificial neural network (ANN) model that can learn to predict the stiffness matrices of 2D finite elements, such as the 8-node quadrilateral element. The computational efficiency and accuracy of the finite elements generated through the ANN model are also checked with existing finite elements through some numerical examples. The results have been found to be consistent with available literature.
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Acknowledgements
The authors gratefully acknowledge the support from SERB, DST under project IMP/2019/000276, and VSSC, ISRO, through MoU No.: ISRO:2020:MOU:NO: 480.
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Nath, S.S., Nath, D., Gautam, S.S. (2023). Design of Efficient Finite Elements Using Deep Learning Approach. In: Sharma, R., Kannojiya, R., Garg, N., Gautam, S.S. (eds) Advances in Engineering Design. FLAME 2022. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-3033-3_2
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DOI: https://doi.org/10.1007/978-981-99-3033-3_2
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