A constitutive neural network for incompressible hyperelastic materials
Name
44379_2025_31_ReferencePDF.pdf
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1.86 MB
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Checksum (MD5)
1397365885ddbac2668b6d49f28319cd
Author(s)
Lee, Sanghee
Bathe, Klaus-Jürgen
Date Issued
August 20, 2025
Journal
Machine Learning for Computational Science and Engineering
Publisher
Springer International Publishing
Citation
Lee, S., Bathe, KJ. A constitutive neural network for incompressible hyperelastic materials. Mach. Learn. Comput. Sci. Eng 1, 31 (2025).
Version
Author's final manuscript
Abstract
We propose a B-spline-based constitutive neural network to model the mechanical behavior of incompressible isotropic materials. The theoretical foundation of this network is the Sussman-Bathe model which interpolates tension–compression test data points and recovers the strain energy function. Our neural network uses regression to self-optimize the knot configurations of the B-splines and to determine a twice differentiable curve of the material response that is closely aligned with the given data points. We address datasets displaying physically complicated behaviors. Through the patch test validation of the constitutive model and illustrative example solutions, we highlight the flexibility inherent in spline-based models and the automated approximation capabilities enabled by neural networks.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Center for Computational Science and Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s44379-025-00031-1
