Finite Strain Homogenization Using a Reduced Basis and Efficient Sampling
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Abstract (arXiv)
The computational homogenization of hyperelastic solids in the geometrically nonlinear context has yet to be treated with sufficient efficiency in order to allow for real-world applications in true multiscale settings. This problem is addressed by a problem-specific surrogate model founded on a reduced basis approximation of the deformation gradient on the microscale. The setup phase is based upon a snapshot POD on deformation gradient fluctuations, in contrast to the widespread displacement-based approach. In order to reduce the computational offline costs, the space of relevant macroscopic stretch tensors is sampled efficiently by employing the Hencky strain. Numerical results show speed-up factors in the order of 5-100 and significantly improved robustness while retaining good accuracy. An open-source demonstrator tool with 50 lines of code emphasizes the simplicity and efficiency of the method.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). First posted on arXiv 1 Apr 2019.
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Citing papers per year: 2021: 1.
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- 2021Polyconvex anisotropic hyperelasticity with neural networkscited by 54
- 2021Polyconvex anisotropic hyperelasticity with neural networks20 Jun 2021