Mechanical MNIST - Multi-Fidelity
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Each dataset in the Mechanical MNIST collection contains the results of 70,000 (60,000 training examples + 10,000 test examples) finite element simulation of a heterogeneous material subject to large deformation. Mechanical MNIST is generated by first converting the MNIST bitmap images (http://www.pymvpa.org/datadb/mnist.html) to 2D heterogeneous blocks of material. Consistent with the MNIST bitmap ($28 \times 28$ pixels), the material domain is a $28 \times 28$ unit square. The material is Neo-Hookean with a varying modulus dictated by the input bitmap. The simulation results included here are the change in strain energy at a fixed level of applied displacement. The cases considered are as follows: *UE: uniaxial extension, full fidelity dataset (fully refined mesh, quadratic triangular elements, applied displacement is 50% of a side length); *EE: equibiaxial extension, full fidelity dataset; *UE-CM-28: uniaxial extension, $28 \times 28 \times 2$ linear triangular elements; *UE-CM-14: uniaxial extension, $14 \times 14 \times 2$ linear triangular elements; *UE-CM-7: uniaxial extension, $7 \times 7 \times 2$ linear triangular elements; *UE-CM-4: uniaxial extension, $4 \times 4 \times 2$ linear triangular elements; *UE-perturb: uniaxial extension, applied displacement is a perturbation (.001 units); and, *UE-CM-28-perturb: uniaxial extension, $28 \times 28 \times 2$ linear triangular elements, applied displacement is a perturbation (.001 units). All simulations are conducted with the FEniCS computing platform (https://fenicsproject.org). The code to reproduce these simulations is hosted on GitHub (https://github.com/elejeune11/Mechanical-MNIST-Transfer-Learning).
The paper "Exploring the potential of transfer learning for metamodels of heterogeneous material deformation" is forthcoming. All code necessary to reproduce the metamodels demonstrated in the manuscript is available on GitHub (https://github.com/elejeune11/Mechanical-MNIST-Transfer-Learning). For questions, please contact Emma Lejeune (email@example.com).
RightsThis dataset is distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 License. The original MNIST bitmaps are from Yann LeCun (Courant Institute, NYU) and Corinna Cortes (Google Labs, New York) on PyMVPA (http://www.pymvpa.org/datadb/mnist.html) licensed with https://creativecommons.org/licenses/by-sa/3.0. The finite element simulations were conducted by Emma Lejeune using the open source software FEniCS (https://fenicsproject.org).